How to Get 97% Accuracy on the German Traffic Signs Validation Dataset


First load the data

In [1]:
# Import pickle files of train, validation, and test sets:
import pickle
import numpy as np
import pandas as pd

training_file = 'traffic-signs-data/train.p'
validation_file = 'traffic-signs-data/valid.p'
testing_file = 'traffic-signs-data/test.p'

with open(training_file, mode='rb') as f:
    train = pickle.load(f)
with open(validation_file, mode='rb') as f:
    valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
    test = pickle.load(f)
    
X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test, y_test = test['features'], test['labels']

# Load sign names:
sign_names = pd.read_csv('signnames.csv')[['ClassId', 'SignName']].values

# Save original sizes and bounding-box coordinates (this will help during preprocessing):
train_sizes= train['sizes']
train_coord = train['coords']

valid_sizes= valid['sizes']
valid_coord = valid['coords']

test_sizes= test['sizes']
test_coord = test['coords']

Let's take a look at the basics

In [2]:
n_train = X_train.shape[0]
n_validation = X_valid.shape[0]
n_test = X_test.shape[0]
image_shape = X_train.shape[1:]
n_classes = len(np.unique(y_train))

print("Number of training examples =", n_train)
print("Number of validation examples =", n_validation)
print("Number of testing examples =", n_test)
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)
Number of training examples = 34799
Number of validation examples = 4410
Number of testing examples = 12630
Image data shape = (32, 32, 3)
Number of classes = 43

Now let's check out the distribution of classes for each set

I like looking at the numbers rather than a graph. I find it easier to ingest the information, so that's what I'll do.

In [3]:
a, b = np.unique(y_train, return_counts=True)
print('Training Data:')
for i in range(len(a)):
    print('{:.20} occurs {} times or {:.0f}% of total'.format(sign_names[a[i], 1], b[i], (b[i]/np.sum(b))*100))
print('')
a, b = np.unique(y_valid, return_counts=True)
print('Validation Data:')
for i in range(len(a)):
    print('{:.20} occurs {} times or {:.0f}% of total'.format(sign_names[a[i], 1], b[i], (b[i]/np.sum(b))*100))
print('')  
a, b = np.unique(y_test, return_counts=True)
print('Test Data:')
for i in range(len(a)):
    print('{:.30} occurs {} times or {:.0f}% of total'.format(sign_names[a[i], 1], b[i], (b[i]/np.sum(b))*100))
Training Data:
Speed limit (20km/h) occurs 180 times or 1% of total
Speed limit (30km/h) occurs 1980 times or 6% of total
Speed limit (50km/h) occurs 2010 times or 6% of total
Speed limit (60km/h) occurs 1260 times or 4% of total
Speed limit (70km/h) occurs 1770 times or 5% of total
Speed limit (80km/h) occurs 1650 times or 5% of total
End of speed limit ( occurs 360 times or 1% of total
Speed limit (100km/h occurs 1290 times or 4% of total
Speed limit (120km/h occurs 1260 times or 4% of total
No passing occurs 1320 times or 4% of total
No passing for vehic occurs 1800 times or 5% of total
Right-of-way at the  occurs 1170 times or 3% of total
Priority road occurs 1890 times or 5% of total
Yield occurs 1920 times or 6% of total
Stop occurs 690 times or 2% of total
No vehicles occurs 540 times or 2% of total
Vehicles over 3.5 me occurs 360 times or 1% of total
No entry occurs 990 times or 3% of total
General caution occurs 1080 times or 3% of total
Dangerous curve to t occurs 180 times or 1% of total
Dangerous curve to t occurs 300 times or 1% of total
Double curve occurs 270 times or 1% of total
Bumpy road occurs 330 times or 1% of total
Slippery road occurs 450 times or 1% of total
Road narrows on the  occurs 240 times or 1% of total
Road work occurs 1350 times or 4% of total
Traffic signals occurs 540 times or 2% of total
Pedestrians occurs 210 times or 1% of total
Children crossing occurs 480 times or 1% of total
Bicycles crossing occurs 240 times or 1% of total
Beware of ice/snow occurs 390 times or 1% of total
Wild animals crossin occurs 690 times or 2% of total
End of all speed and occurs 210 times or 1% of total
Turn right ahead occurs 599 times or 2% of total
Turn left ahead occurs 360 times or 1% of total
Ahead only occurs 1080 times or 3% of total
Go straight or right occurs 330 times or 1% of total
Go straight or left occurs 180 times or 1% of total
Keep right occurs 1860 times or 5% of total
Keep left occurs 270 times or 1% of total
Roundabout mandatory occurs 300 times or 1% of total
End of no passing occurs 210 times or 1% of total
End of no passing by occurs 210 times or 1% of total

Validation Data:
Speed limit (20km/h) occurs 30 times or 1% of total
Speed limit (30km/h) occurs 240 times or 5% of total
Speed limit (50km/h) occurs 240 times or 5% of total
Speed limit (60km/h) occurs 150 times or 3% of total
Speed limit (70km/h) occurs 210 times or 5% of total
Speed limit (80km/h) occurs 210 times or 5% of total
End of speed limit ( occurs 60 times or 1% of total
Speed limit (100km/h occurs 150 times or 3% of total
Speed limit (120km/h occurs 150 times or 3% of total
No passing occurs 150 times or 3% of total
No passing for vehic occurs 210 times or 5% of total
Right-of-way at the  occurs 150 times or 3% of total
Priority road occurs 210 times or 5% of total
Yield occurs 240 times or 5% of total
Stop occurs 90 times or 2% of total
No vehicles occurs 90 times or 2% of total
Vehicles over 3.5 me occurs 60 times or 1% of total
No entry occurs 120 times or 3% of total
General caution occurs 120 times or 3% of total
Dangerous curve to t occurs 30 times or 1% of total
Dangerous curve to t occurs 60 times or 1% of total
Double curve occurs 60 times or 1% of total
Bumpy road occurs 60 times or 1% of total
Slippery road occurs 60 times or 1% of total
Road narrows on the  occurs 30 times or 1% of total
Road work occurs 150 times or 3% of total
Traffic signals occurs 60 times or 1% of total
Pedestrians occurs 30 times or 1% of total
Children crossing occurs 60 times or 1% of total
Bicycles crossing occurs 30 times or 1% of total
Beware of ice/snow occurs 60 times or 1% of total
Wild animals crossin occurs 90 times or 2% of total
End of all speed and occurs 30 times or 1% of total
Turn right ahead occurs 90 times or 2% of total
Turn left ahead occurs 60 times or 1% of total
Ahead only occurs 120 times or 3% of total
Go straight or right occurs 60 times or 1% of total
Go straight or left occurs 30 times or 1% of total
Keep right occurs 210 times or 5% of total
Keep left occurs 30 times or 1% of total
Roundabout mandatory occurs 60 times or 1% of total
End of no passing occurs 30 times or 1% of total
End of no passing by occurs 30 times or 1% of total

Test Data:
Speed limit (20km/h) occurs 60 times or 0% of total
Speed limit (30km/h) occurs 720 times or 6% of total
Speed limit (50km/h) occurs 750 times or 6% of total
Speed limit (60km/h) occurs 450 times or 4% of total
Speed limit (70km/h) occurs 660 times or 5% of total
Speed limit (80km/h) occurs 630 times or 5% of total
End of speed limit (80km/h) occurs 150 times or 1% of total
Speed limit (100km/h) occurs 450 times or 4% of total
Speed limit (120km/h) occurs 450 times or 4% of total
No passing occurs 480 times or 4% of total
No passing for vehicles over 3 occurs 660 times or 5% of total
Right-of-way at the next inter occurs 420 times or 3% of total
Priority road occurs 690 times or 5% of total
Yield occurs 720 times or 6% of total
Stop occurs 270 times or 2% of total
No vehicles occurs 210 times or 2% of total
Vehicles over 3.5 metric tons  occurs 150 times or 1% of total
No entry occurs 360 times or 3% of total
General caution occurs 390 times or 3% of total
Dangerous curve to the left occurs 60 times or 0% of total
Dangerous curve to the right occurs 90 times or 1% of total
Double curve occurs 90 times or 1% of total
Bumpy road occurs 120 times or 1% of total
Slippery road occurs 150 times or 1% of total
Road narrows on the right occurs 90 times or 1% of total
Road work occurs 480 times or 4% of total
Traffic signals occurs 180 times or 1% of total
Pedestrians occurs 60 times or 0% of total
Children crossing occurs 150 times or 1% of total
Bicycles crossing occurs 90 times or 1% of total
Beware of ice/snow occurs 150 times or 1% of total
Wild animals crossing occurs 270 times or 2% of total
End of all speed and passing l occurs 60 times or 0% of total
Turn right ahead occurs 210 times or 2% of total
Turn left ahead occurs 120 times or 1% of total
Ahead only occurs 390 times or 3% of total
Go straight or right occurs 120 times or 1% of total
Go straight or left occurs 60 times or 0% of total
Keep right occurs 690 times or 5% of total
Keep left occurs 90 times or 1% of total
Roundabout mandatory occurs 90 times or 1% of total
End of no passing occurs 60 times or 0% of total
End of no passing by vehicles  occurs 90 times or 1% of total

Nothing too surprising. The distributions look similar. In all three sets, some signs occur more than others. Some of the notable ones are speed-limit, priority road, and yield signs.

Time to look at actual signs

I'll start by printing 80 random signs from each set.

In [4]:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
In [5]:
ii = 1
idx = np.arange(n_train)
np.random.shuffle(idx)
plt.figure(figsize=(45, 45))
for i in range(10*8):
    plt.subplot(2*4,5*2,(i+1))
    plt.title('{:.20}'.format(sign_names[y_train[idx[i*ii]], 1]), fontsize=22)
    plt.imshow(X_train[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
In [6]:
ii = 1
idx = np.arange(n_validation)
np.random.shuffle(idx)
plt.figure(figsize=(45, 45))
for i in range(10*8):
    plt.subplot(2*4,5*2,(i+1))
    plt.title('{:.33}'.format(sign_names[y_valid[idx[i*ii]], 1]), fontsize=22)
    plt.imshow(X_valid[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
In [7]:
ii = 1
idx = np.arange(n_test)
np.random.shuffle(idx)
plt.figure(figsize=(45, 45))
for i in range(10*8):
    plt.subplot(2*4,5*2,(i+1))
    plt.title('{:.33}'.format(sign_names[y_test[idx[i*ii]], 1]), fontsize=22)
    plt.imshow(X_test[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')

There are lots of repeats in the training set . . .

In [8]:
plt.figure(figsize=(15,15))
for i in range(9):
    plt.subplot(3,3,(i+1))
    plt.imshow(X_train[4200+i])
    plt.title(sign_names[y_train[4200+i], 1])

. . . as well as in the validation set

As it happens, the below sign was the most misclassified sign for a network that I ran for 200 epochs and achieved a validation accuracy over 95%. Too bad it repeats many times within the validaiton set, so the one mistake counts as 10+ mistakes :(

In [9]:
plt.figure(figsize=(15,15))
for i in range(9):
    plt.subplot(3,3,(i+1))
    plt.imshow(X_valid[540+i])
    plt.title(sign_names[y_valid[540+i], 1])

How to preprocess the data?

The first five functions augment the training data and are not applied during test time on the validation or test sets. They are applied randomly to each image in each batch so the network never sees the exact same image twice, even if there are repeats :)

random_blur: Applies a Gaussian Noise kernel 50% of time

random_brighten: After noticing that signs in all sets had varying degrees of brightness, which didn't seem to be related to natural light conditions, I used this to randomly brighten or darken an image in a way to resemble images in the validation and test sets. I found out about OpenCV's lookup table from this tutorial.

random_red_adjust: This is poorly named because it adjusts all three color channels. It started out only affecting the red channel, but seemed to help the network generalize, so evolved.

random_rotate: Self-explanatory. I don't flip images and only rotate them by a max of 40 degrees because certain signs, like Left Turn Ahead and speed-limit signs, would confuse the network if flipped.

random_crop: Self-explanatory.

The only preprocessing normalization is to scale the data between 0 and 1.

Since bounding boxes are included, every image is cropped to include only the region within the box and resized to 32x32x3 for uniformity.

Images are kept in color because networks trained on RGB-images performed better for me than those on grayscale. Intuitively, this makes sense because street signs are designed to be different in three ways: shape, images and/or text, and color.

In [7]:
import cv2
import matplotlib.image as mpimg
In [8]:
def random_blur(x, kernel_size=3):
    blur = np.random.randint(1,3)
    if blur == 1:
        return cv2.GaussianBlur(x, (kernel_size, kernel_size), 0)
    if blur == 2:
        return x
    
def random_brighten(x):
    dark = np.random.randint(1,3)
    if dark == 1:
        gamma = np.random.random() * 0.7 + 0.3
    if dark == 2:
        gamma = np.random.random() * 3.5 + 1.1
    invGamma = 1.0 / gamma
    table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8")
    return cv2.LUT(x, table)

def random_red_adjust(x):    
    R = np.random.random() * 0.3 + 0.7
    G = np.random.random() * 0.3 + 0.7
    B = np.random.random() * 0.3 + 0.7
    x[:,:,0] *= R
    x[:,:,1] *= G
    x[:,:,2] *= B
    return x

def random_rotate(x):
    degrees = int((np.random.rand() * 80) - 40)
    rows = x.shape[0]
    cols = x.shape[1]
    M = cv2.getRotationMatrix2D((cols/2,rows/2),degrees,1)
    dst = cv2.warpAffine(x,M,(cols,rows))
    return dst

def random_crop(x):
    n = int(x.shape[0] * 0.2)
    m = int(x.shape[1] * 0.2)
    x1 = np.random.randint(n)
    x2 = np.random.randint(n)
    y1 = np.random.randint(m)
    y2 = np.random.randint(m)
    crop = x[x1:x.shape[1]-x2, y1:x.shape[1]-y2]
    out = cv2.resize(crop, dsize=(32,32))
    return out

def normalize(x):
    return x / 255.

def region_of_interest_train(images, sizes, coords):
    # the reason for the try statement is to ignore illogical bounding boxes or images lacking them
    # the reason for the two if statements is to distinguish between inputs of batch_size and one image
    images_roi = []
    if len(images.shape) == 4:
        for i in range(images.shape[0]):
            try:
                orig_shape = images[i].shape
                resize = cv2.resize(images[i], dsize=tuple(sizes[i]))
                crop = resize[coords[i][0]:coords[i][2], coords[i][1]:coords[i][3], :]
                rand_crop = random_crop(crop)
                rand_bri = random_brighten(rand_crop)
                rand_rot = random_rotate(rand_bri)
                rand_blu = random_blur(rand_rot)
                resize2 = cv2.resize(rand_blu, dsize=tuple(orig_shape[:2]))
                norm = normalize(resize2)
                out = random_red_adjust(norm)
                images_roi.append(out)
            except:
                images_roi.append(normalize(images[i]))
                
    if len(images.shape) == 3:
        orig_shape = images.shape
        resize = cv2.resize(images, dsize=tuple(sizes))
        crop = resize[coords[0]:coords[2], coords[1]:coords[3], :]
        rand_crop = random_crop(crop)
        rand_bri = random_brighten(rand_crop)
        rand_rot = random_rotate(rand_bri)
        rand_blu = random_blur(rand_rot)
        resize2 = cv2.resize(rand_blu, dsize=tuple(orig_shape[:2]))
        norm = normalize(resize2)
        out = random_red_adjust(norm)
        images_roi.append(out)
    return np.array(images_roi)

def get_batches_train(images, labels, sign_names, sizes, coords, batch_num):
    num_images = len(images)
    idx = np.arange(num_images)
    np.random.shuffle(idx)
    for i in range(0, num_images, batch_num):
        batch_y = labels[idx[i:i+batch_num]]
        batch_sizes = sizes[idx[i:i+batch_num]]
        batch_coords = coords[idx[i:i+batch_num]]
        batch_names = sign_names[batch_y, 1]
        images_x = images[idx[i:i+batch_num]]
        batch_x = region_of_interest_train(images_x, batch_sizes, batch_coords).reshape((-1, 32, 32, 3))
        yield batch_x, batch_y, batch_names, batch_sizes, batch_coords

# Seperate get_batches and region_of_interest for test and validation without data augmentation
def region_of_interest_test(images, sizes, coords):
    images_roi = []
    if len(images.shape) == 4:
        for i in range(images.shape[0]):
            try:
                orig_shape = images[i].shape
                resize = cv2.resize(images[i], dsize=tuple(sizes[i]))
                crop = resize[coords[i][0]:coords[i][2], coords[i][1]:coords[i][3], :]
                out = cv2.resize(crop, dsize=tuple(orig_shape[:2]))
                images_roi.append(normalize(out))
            except:
                images_roi.append(normalize(images[i]))
                
    if len(images.shape) == 3:
        orig_shape = images.shape
        resize = cv2.resize(images, dsize=tuple(sizes))
        crop = resize[coords[0]:coords[2], coords[1]:coords[3], :]
        out = cv2.resize(crop, dsize=tuple(orig_shape[:2]))
        images_roi.append(normalize(out))
    return np.array(images_roi)

def get_batches_test(images, labels, sign_names, sizes, coords, batch_num):
    num_images = len(images)
    for i in range(0, num_images, batch_num):
        idx = np.arange(i,i+batch_num)
        batch_y = labels[i:i+batch_num]
        batch_sizes = sizes[i:i+batch_num]
        batch_coords = coords[i:i+batch_num]
        batch_names = sign_names[batch_y, 1]
        images_x = images[i:i+batch_num]
        batch_x = region_of_interest_test(images_x, batch_sizes, batch_coords).reshape((-1, 32, 32, 3))
        yield idx, batch_x, batch_y, batch_names, batch_sizes, batch_coords

Here's what one image looks like when preprocessed eight times

In [10]:
x = X_train[267]
y = y_train[267]
x_name = sign_names[y]
x_coord = train_coord[267]
x_size = train_sizes[267]
x_resize = cv2.resize(x, dsize=tuple(x_size))
plt.figure(figsize=((15,15)))
plt.suptitle(x_name[1], fontsize=20, y=0.91)
plt.subplot(3,3,1)
plt.title('ORIGINAL')
plt.imshow(x)
for i in range(2,10):
    plt.subplot(3,3,i)
    plt.imshow(region_of_interest_train(x, x_size, x_coord).reshape((32,32,3)))

Now compare 10 images from each set before and after preprocessing

Only the train images are randomly augmented, but we'll look at all three sets to make sure everything works properly.

In [11]:
train_roi = region_of_interest_train(X_train, train_sizes, train_coord)
valid_roi = region_of_interest_test(X_valid, valid_sizes, valid_coord)
test_roi = region_of_interest_test(X_test, test_sizes, test_coord)
In [12]:
ii = 3
idx = np.arange(n_train)
np.random.shuffle(idx)
plt.figure(figsize=(15, 6))
plt.suptitle('Train Before', fontsize=16)
for i in range(10):
    plt.subplot(2,5,(i+1))
    plt.title('{:.33}'.format(sign_names[y_train[idx[i*ii]], 1]))
    plt.imshow(X_train[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
    
plt.figure(figsize=(15, 6))
plt.suptitle('Train After', fontsize=16)
for i in range(10):
    plt.subplot(2,5,(i+1))
    plt.title('{:.33}'.format(sign_names[y_train[idx[i*ii]], 1]))
    plt.imshow(train_roi[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
In [13]:
ii = 3
idx = np.arange(n_validation)
np.random.shuffle(idx)
plt.figure(figsize=(15, 6))
plt.suptitle('Validation Before', fontsize=16)
for i in range(10):
    plt.subplot(2,5,(i+1))
    plt.title('{:.33}'.format(sign_names[y_valid[idx[i*ii]], 1]))
    plt.imshow(X_valid[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
    
plt.figure(figsize=(15, 6))
plt.suptitle('Validation After', fontsize=16)
for i in range(10):
    plt.subplot(2,5,(i+1))
    plt.title('{:.33}'.format(sign_names[y_valid[idx[i*ii]], 1]))
    plt.imshow(valid_roi[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
In [14]:
ii = 3
idx = np.arange(n_test)
np.random.shuffle(idx)
plt.figure(figsize=(15, 6))
plt.suptitle('Test Before', fontsize=16)
for i in range(10):
    plt.subplot(2,5,(i+1))
    plt.title('{:.33}'.format(sign_names[y_test[idx[i*ii]], 1]))
    plt.imshow(X_test[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
    
plt.figure(figsize=(15, 6))
plt.suptitle('Test After', fontsize=16)
for i in range(10):
    plt.subplot(2,5,(i+1))
    plt.title('{:.33}'.format(sign_names[y_test[idx[i*ii]], 1]))
    plt.imshow(test_roi[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')

I find it useful to see a bunch of images at once, so here's what 80 preprocessed images from each set look like

In [18]:
ii = 1
idx = np.arange(n_train)
np.random.shuffle(idx)
plt.figure(figsize=(45, 45))
plt.suptitle('80 Train', fontsize=50, y=0.91)
for i in range(10*8):
    plt.subplot(2*4,5*2,(i+1))
    plt.title('{:.20}'.format(sign_names[y_train[idx[i*ii]], 1]), fontsize=22)
    plt.imshow(train_roi[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
In [19]:
ii = 1
idx = np.arange(n_validation)
np.random.shuffle(idx)
plt.figure(figsize=(45, 45))
plt.suptitle('80 Validation', fontsize=50, y=0.91)
for i in range(10*8):
    plt.subplot(2*4,5*2,(i+1))
    plt.title('{:.33}'.format(sign_names[y_valid[idx[i*ii]], 1]), fontsize=22)
    plt.imshow(valid_roi[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
In [20]:
ii = 1
idx = np.arange(n_test)
np.random.shuffle(idx)
plt.figure(figsize=(45, 45))
plt.suptitle('80 Test', fontsize=50, y=0.91)
for i in range(10*8):
    plt.subplot(2*4,5*2,(i+1))
    plt.title('{:.33}'.format(sign_names[y_test[idx[i*ii]], 1]), fontsize=22)
    plt.imshow(test_roi[idx[i*ii]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')

Build the brain!

In [7]:
import tensorflow as tf
from collections import namedtuple
In [8]:
def build_network(n_classes, batch_num, size_mult=64, alpha=0.2):
    tf.reset_default_graph()
    
    drop_rate = tf.placeholder(tf.float32, name='drop_rate')
    is_train = tf.placeholder(tf.bool, name='is_train')
    learn_rate = tf.placeholder(tf.float32, name='learn_rate')
    
    with tf.name_scope('inputs'):
        x = tf.placeholder(tf.float32, [None, 32, 32, 3], name='inputs')
    
    with tf.name_scope('targets'):
        targets = tf.placeholder(tf.int32, [None])
        y_one_hot = tf.one_hot(targets, n_classes, name='y_one_hot')
        y_reshaped = tf.reshape(y_one_hot, [-1, n_classes])
        
    with tf.name_scope('CONV1'):
        x1 = tf.layers.conv2d(x, 2*size_mult, 5, strides=1, padding='same', use_bias=True, name='conv1')
        relu1 = tf.maximum(alpha * x1, x1)
        
    with tf.name_scope('CONV2'):
        x2 = tf.layers.conv2d(relu1, 2*size_mult, 5, strides=2, padding='same', use_bias=True, name='conv2')
        relu2 = tf.maximum(alpha * x2, x2)
        
    with tf.name_scope('CONV3'):
        x3 = tf.layers.conv2d(relu2, 4*size_mult, 5, strides=1, padding='same', use_bias=False, name='conv3')
        bn3 = tf.layers.batch_normalization(x3, training=is_train, name='batch3')
        relu3 = tf.maximum(alpha * bn3, bn3)
        do3 = tf.layers.dropout(relu3, rate=drop_rate)
        
    with tf.name_scope('CONV4'):
        x4 = tf.layers.conv2d(do3, 4*size_mult, 5, strides=2, padding='same', use_bias=True, name='conv4')
        relu4 = tf.maximum(alpha * x4, x4)
        
    with tf.name_scope('CONV5'):   
        x5 = tf.layers.conv2d(relu4, 8*size_mult, 3, strides=1, padding='same', use_bias=True, name='conv5')
        relu5 = tf.maximum(alpha * x5, x5)
        do5 = tf.layers.dropout(relu5, rate=drop_rate)
        
    with tf.name_scope('CONV6'):
        x6 = tf.layers.conv2d(do5, 8*size_mult, 3, strides=2, padding='same', use_bias=True, name='conv6')
        relu6 = tf.maximum(alpha * x6, x6)
    
    with tf.name_scope('CONV7'):
        x7 = tf.layers.conv2d(relu6, 8*size_mult, 3, strides=1, padding='same', use_bias=False, name='conv7')
        bn7 = tf.layers.batch_normalization(x7, training=is_train, name='batch7')
        relu7 = tf.maximum(alpha * bn7, bn7)
        do7 = tf.layers.dropout(relu7, rate=drop_rate)
        
    with tf.name_scope('FC8'):
        x8 = tf.contrib.layers.flatten(do7)
        fc8 = tf.layers.dense(x8, 16 * size_mult, use_bias=True, activation=None, name='fc8')
        relu8 = tf.maximum(alpha * fc8, fc8)
        do8 = tf.layers.dropout(relu8, rate=drop_rate)
        
    with tf.name_scope('logits'):
        logits = tf.layers.dense(do8, n_classes, use_bias=True, activation=None, name='logits')
    
    with tf.name_scope('predictions'):
        preds = tf.nn.softmax(logits, name='predictions')
        correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y_reshaped, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
    
    with tf.name_scope('cost'):
        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped), name='cost')
    
    with tf.name_scope('train'):
        train_opt = tf.train.AdamOptimizer(learn_rate).minimize(cost)
    
    export_nodes = ['x', 'targets', 'drop_rate', 'is_train', 'learn_rate', 'relu1', 'relu2', 'relu3',
                    'relu4', 'relu5', 'relu6', 'relu7', 'correct_pred', 'preds', 'cost', 'accuracy', 'train_opt']
    Graph = namedtuple('Graph', export_nodes)
    local_dict = locals()
    graph = Graph(*[local_dict[each] for each in export_nodes])
    
    return graph

Here's a couple functions that will allow us to view sample validation images and the network's top-5 softmax probabilities for them during training

In [9]:
how_many = 5

def top_n_predictions(pred, top_n=how_many, probs_off=True):
    values, indices = tf.nn.top_k(pred, k=top_n)
    val = np.squeeze(values.eval()).tolist()
    ind = np.squeeze(indices.eval()).tolist()
    if probs_off:
        out = sign_names[ind, 1]
    else:
        out = val
    
    return out

def view_val_with_preds(sess, image, label, sign_name, size, coord, top_n=how_many):
    sample_x = region_of_interest_test(image, size, coord)
    
    feed = {model.x: sample_x.reshape((1,32,32,-1)), 
            model.targets: label.reshape((1,)), 
            model.drop_rate: 0., 
            model.is_train: False}
    pred = sess.run(model.preds, feed_dict=feed)
    top_n_pred_names = top_n_predictions(pred)
    top_n_pred_probs = top_n_predictions(pred, probs_off=False)
    
    plt.figure(figsize=(10, 8))
    plt.suptitle('{:.33}'.format(sign_name), fontsize=16)
        
    plt.subplot(2,2,1)
    y_pos = np.arange(top_n)
    probabilities = np.flip(top_n_pred_probs, 0)
    plt.barh(y_pos, probabilities, align='center', color='blue', tick_label=np.flip(top_n_pred_names, 0))
    plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')
    plt.xlabel('Probability')
    
    plt.subplot(2,2,2)
    plt.imshow(image)
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
        
    plt.subplots_adjust(wspace=0.75)
    plt.show()

Set hyperparameters and create some useful lists

In [10]:
epochs = 200
batch_size = 32
dropout_rate = 0.4
learn_rate = 0.0001
model = build_network(n_classes, learn_rate, batch_size)
saver = tf.train.Saver()
n_batches = int(n_train/batch_size)
steps = 0
train_losses, batch_accs = [], []
total_wrong_indexes = []
print_every = 200
new_val_acc = 0

Learn!

I find it useful to keep an eye on the network as it trains and to offer words of encouragement. Intuitively, this doesn't make sense. But, like dropout, it works well in practice.

In [ ]:
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for e in range(epochs):
        for x, y, _, _, _  in get_batches_train(X_train, y_train, sign_names, train_sizes, train_coord, batch_size):
            steps += 1
            feed = {model.x: x, 
                    model.targets: y, 
                    model.drop_rate: dropout_rate, 
                    model.is_train: True, 
                    model.learn_rate: learn_rate}
            batch_acc, batch_loss, _ = sess.run([model.accuracy, model.cost, model.train_opt], feed_dict=feed)
            train_losses.append(batch_loss)
            batch_accs.append(batch_acc)
            
            if steps % print_every == 0:
                avg_loss = np.mean(train_losses)
                avg_acc = np.mean(batch_accs)
                print('Epoch {}/{}. . . Train Loss {:.4}. . . Train Acc {:.4}'.format(e+1, epochs, avg_loss, avg_acc))

        print('')
        print('Caclulating loss & accuracy on validation set...')
        val_losses, val_accs, wrong_indexes = [], [], []
        for val_idx, val_x, val_y, val_names, _, _  in get_batches_test(X_valid, y_valid, sign_names, 
                                                          valid_sizes, valid_coord, batch_size):
            feed = {model.x: val_x, 
                    model.targets: val_y, 
                    model.drop_rate: 0., 
                    model.is_train: False}
            val_acc, val_cor, val_loss = sess.run([model.accuracy, model.correct_pred, model.cost], feed_dict=feed)
            wrong_idx = [val_idx[i] for i in range(len(val_cor)) if val_cor[i] == False]
            wrong_indexes.append(wrong_idx)
            val_losses.append(val_loss)
            val_accs.append(val_acc)
        avg_val_loss = np.mean(val_losses)
        avg_val_acc = np.mean(val_accs)
        print('Epoch {}/{}. . . Val Loss {:.4}. . . Val Acc {:.4}'.format(e+1, epochs, avg_val_loss, avg_val_acc))
        if avg_val_acc > 0.97:
            if new_val_acc == 0:
                !mkdir checks_97_new
            if avg_val_acc > new_val_acc:
                new_val_acc = avg_val_acc
                saver.save(sess, './checks_97_new/tsigns.ckpt')
                print('Model Saved!')
        print('')
        
        wrong_indexes = np.array([p for sublist in wrong_indexes for p in sublist])
        ind, cnt = np.unique(y_valid[wrong_indexes], return_counts=True)
        wrong_names = [sign_names[ind[i], 1] for i in range(len(ind))]
        for i in range(len(cnt)):
            # Only print sign names of those wrong 5% or more of time
            if (cnt[i]/np.sum(cnt))*100 > 4.9:
                print('<{:.20}> is {:.0f}% of total wrong'.format(wrong_names[i], (cnt[i]/np.sum(cnt))*100))
        print('')
        total_wrong_indexes.append(wrong_indexes)
        
        # Every 5 epochs, let's look at some validation images and the top-5 predicted labels
        if (e+1) % 5 == 0:
            print('Displaying validation samples...')
            num_images = len(X_valid)
            idx = np.arange(num_images)
            np.random.shuffle(idx)
            num_show = 5
            [view_val_with_preds(sess, X_valid[idx[i]], y_valid[idx[i]], sign_names[y_valid[idx[i]], 1], 
                                 valid_sizes[idx[i]], valid_coord[idx[i]]) for i in range(num_show)]
        
        #delete above and use below to only view images that were misclassified:
        #num_images = len(wrong_indexes)
        #idx = np.arange(num_images)
        #np.random.shuffle(idx)
        #num_show = 10
        #[view_val_with_preds(sess, X_valid[wrong_indexes[idx[i]]], y_valid[wrong_indexes[idx[i]]], 
        #                     sign_names[y_valid[wrong_indexes[idx[i]]], 1], valid_sizes[wrong_indexes[idx[i]]], 
        #                     valid_coord[wrong_indexes[idx[i]]]) for i in range(num_show)]
        
        
    print('Model finished training :)')
Epoch 1/200. . . Train Loss 2.883. . . Train Acc 0.2283
Epoch 1/200. . . Train Loss 2.299. . . Train Acc 0.3659
Epoch 1/200. . . Train Loss 1.966. . . Train Acc 0.45
Epoch 1/200. . . Train Loss 1.719. . . Train Acc 0.5143
Epoch 1/200. . . Train Loss 1.536. . . Train Acc 0.5632

Caclulating loss & accuracy on  validation set...
Epoch 1/200. . . Val Loss 3.727. . . Val Acc 0.3444

<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<No passing for vehic> is 6% of total wrong

Epoch 2/200. . . Train Loss 1.392. . . Train Acc 0.6024
Epoch 2/200. . . Train Loss 1.274. . . Train Acc 0.636
Epoch 2/200. . . Train Loss 1.173. . . Train Acc 0.6643
Epoch 2/200. . . Train Loss 1.09. . . Train Acc 0.6881
Epoch 2/200. . . Train Loss 1.019. . . Train Acc 0.7082

Caclulating loss & accuracy on  validation set...
Epoch 2/200. . . Val Loss 3.701. . . Val Acc 0.5401

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (100km/h> is 7% of total wrong
<No passing> is 7% of total wrong
<No passing for vehic> is 5% of total wrong
<Road work> is 5% of total wrong

Epoch 3/200. . . Train Loss 0.9587. . . Train Acc 0.7255
Epoch 3/200. . . Train Loss 0.9056. . . Train Acc 0.7403
Epoch 3/200. . . Train Loss 0.8588. . . Train Acc 0.7534
Epoch 3/200. . . Train Loss 0.8178. . . Train Acc 0.7652
Epoch 3/200. . . Train Loss 0.7812. . . Train Acc 0.7756
Epoch 3/200. . . Train Loss 0.7491. . . Train Acc 0.7848

Caclulating loss & accuracy on  validation set...
Epoch 3/200. . . Val Loss 3.675. . . Val Acc 0.6361

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (100km/h> is 7% of total wrong
<Right-of-way at the > is 6% of total wrong
<Road work> is 9% of total wrong

Epoch 4/200. . . Train Loss 0.7189. . . Train Acc 0.7935
Epoch 4/200. . . Train Loss 0.6918. . . Train Acc 0.8011
Epoch 4/200. . . Train Loss 0.6671. . . Train Acc 0.8081
Epoch 4/200. . . Train Loss 0.645. . . Train Acc 0.8143
Epoch 4/200. . . Train Loss 0.6241. . . Train Acc 0.8203

Caclulating loss & accuracy on  validation set...
Epoch 4/200. . . Val Loss 3.632. . . Val Acc 0.673

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<No passing> is 5% of total wrong
<Road work> is 8% of total wrong

Epoch 5/200. . . Train Loss 0.6052. . . Train Acc 0.8256
Epoch 5/200. . . Train Loss 0.5866. . . Train Acc 0.8308
Epoch 5/200. . . Train Loss 0.5696. . . Train Acc 0.8355
Epoch 5/200. . . Train Loss 0.5541. . . Train Acc 0.84
Epoch 5/200. . . Train Loss 0.54. . . Train Acc 0.844
Epoch 5/200. . . Train Loss 0.5266. . . Train Acc 0.8479

Caclulating loss & accuracy on  validation set...
Epoch 5/200. . . Val Loss 3.58. . . Val Acc 0.7106

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 5% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<Road work> is 9% of total wrong

Displaying validation samples...
Epoch 6/200. . . Train Loss 0.5139. . . Train Acc 0.8516
Epoch 6/200. . . Train Loss 0.5019. . . Train Acc 0.8551
Epoch 6/200. . . Train Loss 0.4897. . . Train Acc 0.8586
Epoch 6/200. . . Train Loss 0.4794. . . Train Acc 0.8615
Epoch 6/200. . . Train Loss 0.4694. . . Train Acc 0.8644

Caclulating loss & accuracy on  validation set...
Epoch 6/200. . . Val Loss 3.468. . . Val Acc 0.7554

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 6% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<Slippery road> is 5% of total wrong
<Road work> is 9% of total wrong

Epoch 7/200. . . Train Loss 0.4597. . . Train Acc 0.8672
Epoch 7/200. . . Train Loss 0.4503. . . Train Acc 0.87
Epoch 7/200. . . Train Loss 0.4416. . . Train Acc 0.8725
Epoch 7/200. . . Train Loss 0.434. . . Train Acc 0.8746
Epoch 7/200. . . Train Loss 0.4259. . . Train Acc 0.877
Epoch 7/200. . . Train Loss 0.4178. . . Train Acc 0.8793

Caclulating loss & accuracy on  validation set...
Epoch 7/200. . . Val Loss 3.311. . . Val Acc 0.7237

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Road work> is 9% of total wrong

Epoch 8/200. . . Train Loss 0.4103. . . Train Acc 0.8815
Epoch 8/200. . . Train Loss 0.4035. . . Train Acc 0.8834
Epoch 8/200. . . Train Loss 0.3968. . . Train Acc 0.8853
Epoch 8/200. . . Train Loss 0.3903. . . Train Acc 0.8871
Epoch 8/200. . . Train Loss 0.3841. . . Train Acc 0.8888

Caclulating loss & accuracy on  validation set...
Epoch 8/200. . . Val Loss 3.142. . . Val Acc 0.7548

<Speed limit (30km/h)> is 12% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<Road work> is 10% of total wrong

Epoch 9/200. . . Train Loss 0.3781. . . Train Acc 0.8905
Epoch 9/200. . . Train Loss 0.3719. . . Train Acc 0.8923
Epoch 9/200. . . Train Loss 0.3664. . . Train Acc 0.8939
Epoch 9/200. . . Train Loss 0.3612. . . Train Acc 0.8954
Epoch 9/200. . . Train Loss 0.3558. . . Train Acc 0.897

Caclulating loss & accuracy on  validation set...
Epoch 9/200. . . Val Loss 2.995. . . Val Acc 0.7237

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<Road work> is 7% of total wrong
<Ahead only> is 6% of total wrong

Epoch 10/200. . . Train Loss 0.3507. . . Train Acc 0.8985
Epoch 10/200. . . Train Loss 0.3462. . . Train Acc 0.8997
Epoch 10/200. . . Train Loss 0.3413. . . Train Acc 0.9012
Epoch 10/200. . . Train Loss 0.3367. . . Train Acc 0.9025
Epoch 10/200. . . Train Loss 0.3323. . . Train Acc 0.9037
Epoch 10/200. . . Train Loss 0.3282. . . Train Acc 0.9049

Caclulating loss & accuracy on  validation set...
Epoch 10/200. . . Val Loss 2.696. . . Val Acc 0.7831

<Speed limit (30km/h)> is 9% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 6% of total wrong
<Speed limit (100km/h> is 9% of total wrong
<Speed limit (120km/h> is 9% of total wrong
<Road work> is 9% of total wrong

Displaying validation samples...
Epoch 11/200. . . Train Loss 0.3242. . . Train Acc 0.9061
Epoch 11/200. . . Train Loss 0.3201. . . Train Acc 0.9072
Epoch 11/200. . . Train Loss 0.3164. . . Train Acc 0.9083
Epoch 11/200. . . Train Loss 0.3125. . . Train Acc 0.9094
Epoch 11/200. . . Train Loss 0.3087. . . Train Acc 0.9105

Caclulating loss & accuracy on  validation set...
Epoch 11/200. . . Val Loss 2.476. . . Val Acc 0.7842

<Speed limit (30km/h)> is 9% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (100km/h> is 7% of total wrong
<Speed limit (120km/h> is 9% of total wrong
<Road work> is 9% of total wrong

Epoch 12/200. . . Train Loss 0.305. . . Train Acc 0.9116
Epoch 12/200. . . Train Loss 0.3013. . . Train Acc 0.9127
Epoch 12/200. . . Train Loss 0.2977. . . Train Acc 0.9137
Epoch 12/200. . . Train Loss 0.2946. . . Train Acc 0.9146
Epoch 12/200. . . Train Loss 0.2914. . . Train Acc 0.9155
Epoch 12/200. . . Train Loss 0.2886. . . Train Acc 0.9163

Caclulating loss & accuracy on  validation set...
Epoch 12/200. . . Val Loss 2.218. . . Val Acc 0.7867

<Speed limit (30km/h)> is 9% of total wrong
<Speed limit (70km/h)> is 8% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<Road work> is 8% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 13/200. . . Train Loss 0.2855. . . Train Acc 0.9172
Epoch 13/200. . . Train Loss 0.2825. . . Train Acc 0.918
Epoch 13/200. . . Train Loss 0.2794. . . Train Acc 0.9189
Epoch 13/200. . . Train Loss 0.2766. . . Train Acc 0.9197
Epoch 13/200. . . Train Loss 0.2741. . . Train Acc 0.9204

Caclulating loss & accuracy on  validation set...
Epoch 13/200. . . Val Loss 1.974. . . Val Acc 0.7996

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<Road work> is 7% of total wrong

Epoch 14/200. . . Train Loss 0.2714. . . Train Acc 0.9212
Epoch 14/200. . . Train Loss 0.2687. . . Train Acc 0.922
Epoch 14/200. . . Train Loss 0.2661. . . Train Acc 0.9227
Epoch 14/200. . . Train Loss 0.2637. . . Train Acc 0.9234
Epoch 14/200. . . Train Loss 0.2615. . . Train Acc 0.924
Epoch 14/200. . . Train Loss 0.259. . . Train Acc 0.9247

Caclulating loss & accuracy on  validation set...
Epoch 14/200. . . Val Loss 1.512. . . Val Acc 0.7869

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (70km/h)> is 8% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<Road work> is 9% of total wrong

Epoch 15/200. . . Train Loss 0.2567. . . Train Acc 0.9254
Epoch 15/200. . . Train Loss 0.2544. . . Train Acc 0.9261
Epoch 15/200. . . Train Loss 0.2522. . . Train Acc 0.9267
Epoch 15/200. . . Train Loss 0.2499. . . Train Acc 0.9274
Epoch 15/200. . . Train Loss 0.2477. . . Train Acc 0.928

Caclulating loss & accuracy on  validation set...
Epoch 15/200. . . Val Loss 1.579. . . Val Acc 0.8236

<Speed limit (30km/h)> is 9% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 8% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (120km/h> is 9% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 9% of total wrong

Displaying validation samples...
Epoch 16/200. . . Train Loss 0.2457. . . Train Acc 0.9286
Epoch 16/200. . . Train Loss 0.2436. . . Train Acc 0.9292
Epoch 16/200. . . Train Loss 0.2415. . . Train Acc 0.9298
Epoch 16/200. . . Train Loss 0.2395. . . Train Acc 0.9304
Epoch 16/200. . . Train Loss 0.2377. . . Train Acc 0.9309
Epoch 16/200. . . Train Loss 0.2359. . . Train Acc 0.9314

Caclulating loss & accuracy on  validation set...
Epoch 16/200. . . Val Loss 1.249. . . Val Acc 0.81

<Speed limit (30km/h)> is 9% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 8% of total wrong
<Speed limit (80km/h)> is 8% of total wrong
<Speed limit (120km/h> is 12% of total wrong
<Road work> is 8% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 17/200. . . Train Loss 0.234. . . Train Acc 0.932
Epoch 17/200. . . Train Loss 0.2321. . . Train Acc 0.9325
Epoch 17/200. . . Train Loss 0.2303. . . Train Acc 0.933
Epoch 17/200. . . Train Loss 0.2285. . . Train Acc 0.9335
Epoch 17/200. . . Train Loss 0.2268. . . Train Acc 0.934

Caclulating loss & accuracy on  validation set...
Epoch 17/200. . . Val Loss 1.086. . . Val Acc 0.8057

<Speed limit (30km/h)> is 11% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<Road work> is 8% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 18/200. . . Train Loss 0.225. . . Train Acc 0.9345
Epoch 18/200. . . Train Loss 0.2234. . . Train Acc 0.935
Epoch 18/200. . . Train Loss 0.2219. . . Train Acc 0.9355
Epoch 18/200. . . Train Loss 0.2202. . . Train Acc 0.9359
Epoch 18/200. . . Train Loss 0.2188. . . Train Acc 0.9363

Caclulating loss & accuracy on  validation set...
Epoch 18/200. . . Val Loss 1.016. . . Val Acc 0.7903

<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (120km/h> is 16% of total wrong
<Road work> is 7% of total wrong

Epoch 19/200. . . Train Loss 0.2173. . . Train Acc 0.9368
Epoch 19/200. . . Train Loss 0.2159. . . Train Acc 0.9372
Epoch 19/200. . . Train Loss 0.2143. . . Train Acc 0.9376
Epoch 19/200. . . Train Loss 0.2128. . . Train Acc 0.9381
Epoch 19/200. . . Train Loss 0.2112. . . Train Acc 0.9385
Epoch 19/200. . . Train Loss 0.2098. . . Train Acc 0.9389

Caclulating loss & accuracy on  validation set...
Epoch 19/200. . . Val Loss 0.8682. . . Val Acc 0.815

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (50km/h)> is 12% of total wrong
<Speed limit (70km/h)> is 6% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<Road work> is 10% of total wrong

Epoch 20/200. . . Train Loss 0.2085. . . Train Acc 0.9393
Epoch 20/200. . . Train Loss 0.2071. . . Train Acc 0.9397
Epoch 20/200. . . Train Loss 0.2058. . . Train Acc 0.9401
Epoch 20/200. . . Train Loss 0.2045. . . Train Acc 0.9405
Epoch 20/200. . . Train Loss 0.2031. . . Train Acc 0.9409

Caclulating loss & accuracy on  validation set...
Epoch 20/200. . . Val Loss 0.6937. . . Val Acc 0.8501

<Speed limit (30km/h)> is 12% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 8% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<Road work> is 10% of total wrong

Displaying validation samples...
Epoch 21/200. . . Train Loss 0.2019. . . Train Acc 0.9412
Epoch 21/200. . . Train Loss 0.2005. . . Train Acc 0.9416
Epoch 21/200. . . Train Loss 0.1993. . . Train Acc 0.942
Epoch 21/200. . . Train Loss 0.1981. . . Train Acc 0.9424
Epoch 21/200. . . Train Loss 0.197. . . Train Acc 0.9427
Epoch 21/200. . . Train Loss 0.1958. . . Train Acc 0.943

Caclulating loss & accuracy on  validation set...
Epoch 21/200. . . Val Loss 0.654. . . Val Acc 0.8449

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (70km/h)> is 11% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 8% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 22/200. . . Train Loss 0.1946. . . Train Acc 0.9433
Epoch 22/200. . . Train Loss 0.1934. . . Train Acc 0.9437
Epoch 22/200. . . Train Loss 0.1922. . . Train Acc 0.944
Epoch 22/200. . . Train Loss 0.1911. . . Train Acc 0.9444
Epoch 22/200. . . Train Loss 0.19. . . Train Acc 0.9447

Caclulating loss & accuracy on  validation set...
Epoch 22/200. . . Val Loss 0.616. . . Val Acc 0.8492

<Speed limit (30km/h)> is 9% of total wrong
<Speed limit (70km/h)> is 10% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 9% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 23/200. . . Train Loss 0.1889. . . Train Acc 0.945
Epoch 23/200. . . Train Loss 0.1878. . . Train Acc 0.9453
Epoch 23/200. . . Train Loss 0.1868. . . Train Acc 0.9456
Epoch 23/200. . . Train Loss 0.1857. . . Train Acc 0.9459
Epoch 23/200. . . Train Loss 0.1847. . . Train Acc 0.9462
Epoch 23/200. . . Train Loss 0.1836. . . Train Acc 0.9465

Caclulating loss & accuracy on  validation set...
Epoch 23/200. . . Val Loss 0.4907. . . Val Acc 0.8648

<Speed limit (20km/h)> is 5% of total wrong
<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (50km/h)> is 8% of total wrong
<Speed limit (70km/h)> is 5% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 5% of total wrong
<Children crossing> is 5% of total wrong
<Roundabout mandatory> is 5% of total wrong
<End of no passing by> is 5% of total wrong

Epoch 24/200. . . Train Loss 0.1825. . . Train Acc 0.9468
Epoch 24/200. . . Train Loss 0.1815. . . Train Acc 0.9471
Epoch 24/200. . . Train Loss 0.1806. . . Train Acc 0.9474
Epoch 24/200. . . Train Loss 0.1798. . . Train Acc 0.9476
Epoch 24/200. . . Train Loss 0.1788. . . Train Acc 0.9479

Caclulating loss & accuracy on  validation set...
Epoch 24/200. . . Val Loss 0.7226. . . Val Acc 0.8243

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 14% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<Road work> is 9% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 25/200. . . Train Loss 0.1779. . . Train Acc 0.9482
Epoch 25/200. . . Train Loss 0.1769. . . Train Acc 0.9485
Epoch 25/200. . . Train Loss 0.176. . . Train Acc 0.9487
Epoch 25/200. . . Train Loss 0.1751. . . Train Acc 0.949
Epoch 25/200. . . Train Loss 0.1742. . . Train Acc 0.9492
Epoch 25/200. . . Train Loss 0.1734. . . Train Acc 0.9495

Caclulating loss & accuracy on  validation set...
Epoch 25/200. . . Val Loss 0.4461. . . Val Acc 0.8762

<Speed limit (20km/h)> is 5% of total wrong
<Speed limit (30km/h)> is 11% of total wrong
<Speed limit (70km/h)> is 9% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<Right-of-way at the > is 5% of total wrong
<Road work> is 8% of total wrong
<Roundabout mandatory> is 9% of total wrong
<End of no passing by> is 5% of total wrong

Displaying validation samples...
Epoch 26/200. . . Train Loss 0.1725. . . Train Acc 0.9497
Epoch 26/200. . . Train Loss 0.1715. . . Train Acc 0.95
Epoch 26/200. . . Train Loss 0.1706. . . Train Acc 0.9503
Epoch 26/200. . . Train Loss 0.1698. . . Train Acc 0.9505
Epoch 26/200. . . Train Loss 0.169. . . Train Acc 0.9508

Caclulating loss & accuracy on  validation set...
Epoch 26/200. . . Val Loss 0.657. . . Val Acc 0.8517

<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 9% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<Right-of-way at the > is 5% of total wrong
<Road work> is 11% of total wrong

Epoch 27/200. . . Train Loss 0.1681. . . Train Acc 0.951
Epoch 27/200. . . Train Loss 0.1673. . . Train Acc 0.9512
Epoch 27/200. . . Train Loss 0.1664. . . Train Acc 0.9515
Epoch 27/200. . . Train Loss 0.1656. . . Train Acc 0.9517
Epoch 27/200. . . Train Loss 0.1648. . . Train Acc 0.952

Caclulating loss & accuracy on  validation set...
Epoch 27/200. . . Val Loss 0.4801. . . Val Acc 0.8805

<Speed limit (20km/h)> is 5% of total wrong
<Speed limit (30km/h)> is 12% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (70km/h)> is 9% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<No entry> is 5% of total wrong
<Road work> is 12% of total wrong
<Roundabout mandatory> is 6% of total wrong
<End of no passing by> is 6% of total wrong

Epoch 28/200. . . Train Loss 0.1639. . . Train Acc 0.9522
Epoch 28/200. . . Train Loss 0.1632. . . Train Acc 0.9524
Epoch 28/200. . . Train Loss 0.1626. . . Train Acc 0.9526
Epoch 28/200. . . Train Loss 0.1618. . . Train Acc 0.9528
Epoch 28/200. . . Train Loss 0.1611. . . Train Acc 0.953
Epoch 28/200. . . Train Loss 0.1604. . . Train Acc 0.9532

Caclulating loss & accuracy on  validation set...
Epoch 28/200. . . Val Loss 0.4728. . . Val Acc 0.8782

<Speed limit (20km/h)> is 5% of total wrong
<Speed limit (30km/h)> is 11% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<Road work> is 13% of total wrong
<Children crossing> is 5% of total wrong
<Roundabout mandatory> is 7% of total wrong

Epoch 29/200. . . Train Loss 0.1597. . . Train Acc 0.9535
Epoch 29/200. . . Train Loss 0.159. . . Train Acc 0.9536
Epoch 29/200. . . Train Loss 0.1584. . . Train Acc 0.9538
Epoch 29/200. . . Train Loss 0.1577. . . Train Acc 0.954
Epoch 29/200. . . Train Loss 0.157. . . Train Acc 0.9542

Caclulating loss & accuracy on  validation set...
Epoch 29/200. . . Val Loss 0.683. . . Val Acc 0.8562

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 10% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 10% of total wrong
<Children crossing> is 6% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 30/200. . . Train Loss 0.1563. . . Train Acc 0.9544
Epoch 30/200. . . Train Loss 0.1556. . . Train Acc 0.9546
Epoch 30/200. . . Train Loss 0.155. . . Train Acc 0.9548
Epoch 30/200. . . Train Loss 0.1542. . . Train Acc 0.955
Epoch 30/200. . . Train Loss 0.1537. . . Train Acc 0.9552
Epoch 30/200. . . Train Loss 0.153. . . Train Acc 0.9554

Caclulating loss & accuracy on  validation set...
Epoch 30/200. . . Val Loss 0.8528. . . Val Acc 0.8494

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 9% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<Road work> is 9% of total wrong

Displaying validation samples...
Epoch 31/200. . . Train Loss 0.1524. . . Train Acc 0.9556
Epoch 31/200. . . Train Loss 0.1517. . . Train Acc 0.9558
Epoch 31/200. . . Train Loss 0.1511. . . Train Acc 0.9559
Epoch 31/200. . . Train Loss 0.1505. . . Train Acc 0.9561
Epoch 31/200. . . Train Loss 0.1499. . . Train Acc 0.9563

Caclulating loss & accuracy on  validation set...
Epoch 31/200. . . Val Loss 0.5181. . . Val Acc 0.8863

<Speed limit (20km/h)> is 6% of total wrong
<Speed limit (30km/h)> is 15% of total wrong
<Speed limit (100km/h> is 7% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 13% of total wrong
<Roundabout mandatory> is 8% of total wrong

Epoch 32/200. . . Train Loss 0.1493. . . Train Acc 0.9564
Epoch 32/200. . . Train Loss 0.1487. . . Train Acc 0.9566
Epoch 32/200. . . Train Loss 0.1481. . . Train Acc 0.9568
Epoch 32/200. . . Train Loss 0.1474. . . Train Acc 0.957
Epoch 32/200. . . Train Loss 0.1468. . . Train Acc 0.9571
Epoch 32/200. . . Train Loss 0.1462. . . Train Acc 0.9573

Caclulating loss & accuracy on  validation set...
Epoch 32/200. . . Val Loss 0.6105. . . Val Acc 0.8793

<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (100km/h> is 11% of total wrong
<Speed limit (120km/h> is 14% of total wrong
<No entry> is 6% of total wrong
<Road work> is 15% of total wrong
<Pedestrians> is 5% of total wrong
<Children crossing> is 5% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 33/200. . . Train Loss 0.1457. . . Train Acc 0.9575
Epoch 33/200. . . Train Loss 0.1451. . . Train Acc 0.9576
Epoch 33/200. . . Train Loss 0.1446. . . Train Acc 0.9578
Epoch 33/200. . . Train Loss 0.144. . . Train Acc 0.958
Epoch 33/200. . . Train Loss 0.1434. . . Train Acc 0.9581

Caclulating loss & accuracy on  validation set...
Epoch 33/200. . . Val Loss 0.6118. . . Val Acc 0.8852

<Speed limit (20km/h)> is 6% of total wrong
<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (70km/h)> is 10% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 13% of total wrong
<No entry> is 6% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 6% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 34/200. . . Train Loss 0.1428. . . Train Acc 0.9583
Epoch 34/200. . . Train Loss 0.1423. . . Train Acc 0.9585
Epoch 34/200. . . Train Loss 0.1418. . . Train Acc 0.9586
Epoch 34/200. . . Train Loss 0.1413. . . Train Acc 0.9587
Epoch 34/200. . . Train Loss 0.1408. . . Train Acc 0.9589

Caclulating loss & accuracy on  validation set...
Epoch 34/200. . . Val Loss 0.8021. . . Val Acc 0.8585

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 10% of total wrong
<Speed limit (100km/h> is 8% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<Road work> is 9% of total wrong
<Wild animals crossin> is 6% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 35/200. . . Train Loss 0.1403. . . Train Acc 0.959
Epoch 35/200. . . Train Loss 0.1398. . . Train Acc 0.9592
Epoch 35/200. . . Train Loss 0.1392. . . Train Acc 0.9593
Epoch 35/200. . . Train Loss 0.1387. . . Train Acc 0.9595
Epoch 35/200. . . Train Loss 0.1382. . . Train Acc 0.9596
Epoch 35/200. . . Train Loss 0.1378. . . Train Acc 0.9598

Caclulating loss & accuracy on  validation set...
Epoch 35/200. . . Val Loss 0.6505. . . Val Acc 0.9

<Speed limit (20km/h)> is 5% of total wrong
<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (70km/h)> is 14% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 15% of total wrong
<Road work> is 8% of total wrong
<Roundabout mandatory> is 7% of total wrong

Displaying validation samples...
Epoch 36/200. . . Train Loss 0.1373. . . Train Acc 0.9599
Epoch 36/200. . . Train Loss 0.1368. . . Train Acc 0.96
Epoch 36/200. . . Train Loss 0.1363. . . Train Acc 0.9602
Epoch 36/200. . . Train Loss 0.1358. . . Train Acc 0.9603
Epoch 36/200. . . Train Loss 0.1353. . . Train Acc 0.9605

Caclulating loss & accuracy on  validation set...
Epoch 36/200. . . Val Loss 0.3887. . . Val Acc 0.9234

<Speed limit (30km/h)> is 9% of total wrong
<Speed limit (70km/h)> is 5% of total wrong
<Speed limit (100km/h> is 9% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<No entry> is 5% of total wrong
<Double curve> is 8% of total wrong
<Road work> is 11% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 37/200. . . Train Loss 0.1348. . . Train Acc 0.9606
Epoch 37/200. . . Train Loss 0.1343. . . Train Acc 0.9607
Epoch 37/200. . . Train Loss 0.1339. . . Train Acc 0.9609
Epoch 37/200. . . Train Loss 0.1335. . . Train Acc 0.961
Epoch 37/200. . . Train Loss 0.133. . . Train Acc 0.9611
Epoch 37/200. . . Train Loss 0.1326. . . Train Acc 0.9612

Caclulating loss & accuracy on  validation set...
Epoch 37/200. . . Val Loss 0.8658. . . Val Acc 0.8834

<Speed limit (30km/h)> is 5% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (70km/h)> is 10% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 12% of total wrong
<Road work> is 13% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 38/200. . . Train Loss 0.1322. . . Train Acc 0.9614
Epoch 38/200. . . Train Loss 0.1317. . . Train Acc 0.9615
Epoch 38/200. . . Train Loss 0.1313. . . Train Acc 0.9616
Epoch 38/200. . . Train Loss 0.1308. . . Train Acc 0.9618
Epoch 38/200. . . Train Loss 0.1303. . . Train Acc 0.9619

Caclulating loss & accuracy on  validation set...
Epoch 38/200. . . Val Loss 0.7369. . . Val Acc 0.9082

<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (70km/h)> is 6% of total wrong
<Speed limit (100km/h> is 8% of total wrong
<Speed limit (120km/h> is 18% of total wrong
<No entry> is 5% of total wrong
<Road work> is 14% of total wrong
<Roundabout mandatory> is 8% of total wrong

Epoch 39/200. . . Train Loss 0.1299. . . Train Acc 0.962
Epoch 39/200. . . Train Loss 0.1295. . . Train Acc 0.9622
Epoch 39/200. . . Train Loss 0.1291. . . Train Acc 0.9623
Epoch 39/200. . . Train Loss 0.1287. . . Train Acc 0.9624
Epoch 39/200. . . Train Loss 0.1282. . . Train Acc 0.9625
Epoch 39/200. . . Train Loss 0.1278. . . Train Acc 0.9627

Caclulating loss & accuracy on  validation set...
Epoch 39/200. . . Val Loss 0.7949. . . Val Acc 0.91

<Speed limit (70km/h)> is 14% of total wrong
<Speed limit (120km/h> is 19% of total wrong
<Vehicles over 3.5 me> is 7% of total wrong
<Road work> is 13% of total wrong
<Roundabout mandatory> is 8% of total wrong

Epoch 40/200. . . Train Loss 0.1274. . . Train Acc 0.9628
Epoch 40/200. . . Train Loss 0.127. . . Train Acc 0.9629
Epoch 40/200. . . Train Loss 0.1266. . . Train Acc 0.963
Epoch 40/200. . . Train Loss 0.1262. . . Train Acc 0.9631
Epoch 40/200. . . Train Loss 0.1258. . . Train Acc 0.9632

Caclulating loss & accuracy on  validation set...
Epoch 40/200. . . Val Loss 0.7481. . . Val Acc 0.8953

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (50km/h)> is 10% of total wrong
<Speed limit (70km/h)> is 11% of total wrong
<Speed limit (100km/h> is 8% of total wrong
<Speed limit (120km/h> is 13% of total wrong
<Vehicles over 3.5 me> is 5% of total wrong
<Road work> is 7% of total wrong

Displaying validation samples...
Epoch 41/200. . . Train Loss 0.1255. . . Train Acc 0.9633
Epoch 41/200. . . Train Loss 0.1251. . . Train Acc 0.9635
Epoch 41/200. . . Train Loss 0.1247. . . Train Acc 0.9636
Epoch 41/200. . . Train Loss 0.1243. . . Train Acc 0.9637
Epoch 41/200. . . Train Loss 0.1239. . . Train Acc 0.9638
Epoch 41/200. . . Train Loss 0.1236. . . Train Acc 0.9639

Caclulating loss & accuracy on  validation set...
Epoch 41/200. . . Val Loss 0.643. . . Val Acc 0.9149

<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 17% of total wrong
<No entry> is 5% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 8% of total wrong

Epoch 42/200. . . Train Loss 0.1232. . . Train Acc 0.964
Epoch 42/200. . . Train Loss 0.1228. . . Train Acc 0.9641
Epoch 42/200. . . Train Loss 0.1225. . . Train Acc 0.9642
Epoch 42/200. . . Train Loss 0.122. . . Train Acc 0.9643
Epoch 42/200. . . Train Loss 0.1217. . . Train Acc 0.9644

Caclulating loss & accuracy on  validation set...
Epoch 42/200. . . Val Loss 0.5582. . . Val Acc 0.9316

<Speed limit (20km/h)> is 10% of total wrong
<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (120km/h> is 13% of total wrong
<No passing> is 7% of total wrong
<No entry> is 7% of total wrong
<Slippery road> is 5% of total wrong
<Pedestrians> is 7% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 43/200. . . Train Loss 0.1213. . . Train Acc 0.9645
Epoch 43/200. . . Train Loss 0.1209. . . Train Acc 0.9646
Epoch 43/200. . . Train Loss 0.1206. . . Train Acc 0.9647
Epoch 43/200. . . Train Loss 0.1202. . . Train Acc 0.9649
Epoch 43/200. . . Train Loss 0.1199. . . Train Acc 0.9649

Caclulating loss & accuracy on  validation set...
Epoch 43/200. . . Val Loss 0.5556. . . Val Acc 0.934

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 12% of total wrong
<Speed limit (70km/h)> is 8% of total wrong
<Speed limit (100km/h> is 10% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 7% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 7% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 44/200. . . Train Loss 0.1196. . . Train Acc 0.965
Epoch 44/200. . . Train Loss 0.1192. . . Train Acc 0.9651
Epoch 44/200. . . Train Loss 0.1188. . . Train Acc 0.9653
Epoch 44/200. . . Train Loss 0.1185. . . Train Acc 0.9654
Epoch 44/200. . . Train Loss 0.1181. . . Train Acc 0.9654
Epoch 44/200. . . Train Loss 0.1178. . . Train Acc 0.9655

Caclulating loss & accuracy on  validation set...
Epoch 44/200. . . Val Loss 0.5975. . . Val Acc 0.9219

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (70km/h)> is 14% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<No entry> is 5% of total wrong
<Road work> is 16% of total wrong
<Roundabout mandatory> is 9% of total wrong

Epoch 45/200. . . Train Loss 0.1175. . . Train Acc 0.9656
Epoch 45/200. . . Train Loss 0.1171. . . Train Acc 0.9657
Epoch 45/200. . . Train Loss 0.1168. . . Train Acc 0.9658
Epoch 45/200. . . Train Loss 0.1165. . . Train Acc 0.9659
Epoch 45/200. . . Train Loss 0.1162. . . Train Acc 0.966

Caclulating loss & accuracy on  validation set...
Epoch 45/200. . . Val Loss 1.341. . . Val Acc 0.8997

<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (70km/h)> is 13% of total wrong
<Speed limit (100km/h> is 8% of total wrong
<Speed limit (120km/h> is 13% of total wrong
<No entry> is 7% of total wrong
<Road narrows on the > is 7% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 5% of total wrong

Displaying validation samples...
Epoch 46/200. . . Train Loss 0.1159. . . Train Acc 0.9661
Epoch 46/200. . . Train Loss 0.1155. . . Train Acc 0.9662
Epoch 46/200. . . Train Loss 0.1152. . . Train Acc 0.9663
Epoch 46/200. . . Train Loss 0.1149. . . Train Acc 0.9664
Epoch 46/200. . . Train Loss 0.1145. . . Train Acc 0.9665
Epoch 46/200. . . Train Loss 0.1142. . . Train Acc 0.9666

Caclulating loss & accuracy on  validation set...
Epoch 46/200. . . Val Loss 0.8713. . . Val Acc 0.913

<Speed limit (70km/h)> is 11% of total wrong
<Speed limit (100km/h> is 8% of total wrong
<Speed limit (120km/h> is 16% of total wrong
<Road work> is 13% of total wrong
<Roundabout mandatory> is 13% of total wrong

Epoch 47/200. . . Train Loss 0.1139. . . Train Acc 0.9667
Epoch 47/200. . . Train Loss 0.1136. . . Train Acc 0.9668
Epoch 47/200. . . Train Loss 0.1133. . . Train Acc 0.9668
Epoch 47/200. . . Train Loss 0.113. . . Train Acc 0.9669
Epoch 47/200. . . Train Loss 0.1127. . . Train Acc 0.967

Caclulating loss & accuracy on  validation set...
Epoch 47/200. . . Val Loss 0.8942. . . Val Acc 0.9036

<Speed limit (50km/h)> is 9% of total wrong
<Speed limit (70km/h)> is 9% of total wrong
<Speed limit (100km/h> is 7% of total wrong
<Speed limit (120km/h> is 14% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 8% of total wrong
<Children crossing> is 7% of total wrong
<Roundabout mandatory> is 8% of total wrong

Epoch 48/200. . . Train Loss 0.1124. . . Train Acc 0.9671
Epoch 48/200. . . Train Loss 0.1121. . . Train Acc 0.9672
Epoch 48/200. . . Train Loss 0.1118. . . Train Acc 0.9673
Epoch 48/200. . . Train Loss 0.1116. . . Train Acc 0.9673
Epoch 48/200. . . Train Loss 0.1113. . . Train Acc 0.9674
Epoch 48/200. . . Train Loss 0.1111. . . Train Acc 0.9675

Caclulating loss & accuracy on  validation set...
Epoch 48/200. . . Val Loss 0.6683. . . Val Acc 0.9365

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 8% of total wrong
<Speed limit (120km/h> is 19% of total wrong
<Vehicles over 3.5 me> is 5% of total wrong
<No entry> is 9% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 5% of total wrong

Epoch 49/200. . . Train Loss 0.1108. . . Train Acc 0.9676
Epoch 49/200. . . Train Loss 0.1105. . . Train Acc 0.9677
Epoch 49/200. . . Train Loss 0.1102. . . Train Acc 0.9678
Epoch 49/200. . . Train Loss 0.1098. . . Train Acc 0.9679
Epoch 49/200. . . Train Loss 0.1095. . . Train Acc 0.9679

Caclulating loss & accuracy on  validation set...
Epoch 49/200. . . Val Loss 0.7051. . . Val Acc 0.9278

<Speed limit (120km/h> is 10% of total wrong
<Vehicles over 3.5 me> is 9% of total wrong
<No entry> is 6% of total wrong
<Road work> is 17% of total wrong
<Pedestrians> is 8% of total wrong
<Children crossing> is 6% of total wrong

Epoch 50/200. . . Train Loss 0.1093. . . Train Acc 0.968
Epoch 50/200. . . Train Loss 0.109. . . Train Acc 0.9681
Epoch 50/200. . . Train Loss 0.1087. . . Train Acc 0.9682
Epoch 50/200. . . Train Loss 0.1085. . . Train Acc 0.9682
Epoch 50/200. . . Train Loss 0.1082. . . Train Acc 0.9683
Epoch 50/200. . . Train Loss 0.108. . . Train Acc 0.9684

Caclulating loss & accuracy on  validation set...
Epoch 50/200. . . Val Loss 0.6961. . . Val Acc 0.9457

<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (100km/h> is 8% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<Vehicles over 3.5 me> is 10% of total wrong
<No entry> is 11% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 9% of total wrong
<Roundabout mandatory> is 7% of total wrong

Displaying validation samples...
Epoch 51/200. . . Train Loss 0.1077. . . Train Acc 0.9685
Epoch 51/200. . . Train Loss 0.1074. . . Train Acc 0.9686
Epoch 51/200. . . Train Loss 0.1071. . . Train Acc 0.9686
Epoch 51/200. . . Train Loss 0.1069. . . Train Acc 0.9687
Epoch 51/200. . . Train Loss 0.1067. . . Train Acc 0.9688

Caclulating loss & accuracy on  validation set...
Epoch 51/200. . . Val Loss 0.4898. . . Val Acc 0.9571
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (50km/h)> is 8% of total wrong
<Speed limit (70km/h)> is 11% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 5% of total wrong
<Double curve> is 8% of total wrong
<Pedestrians> is 8% of total wrong
<Roundabout mandatory> is 7% of total wrong

Epoch 52/200. . . Train Loss 0.1065. . . Train Acc 0.9688
Epoch 52/200. . . Train Loss 0.1062. . . Train Acc 0.9689
Epoch 52/200. . . Train Loss 0.1059. . . Train Acc 0.969
Epoch 52/200. . . Train Loss 0.1057. . . Train Acc 0.9691
Epoch 52/200. . . Train Loss 0.1055. . . Train Acc 0.9691

Caclulating loss & accuracy on  validation set...
Epoch 52/200. . . Val Loss 0.7083. . . Val Acc 0.9461

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (70km/h)> is 5% of total wrong
<Speed limit (120km/h> is 15% of total wrong
<Vehicles over 3.5 me> is 9% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 5% of total wrong
<Double curve> is 8% of total wrong
<Pedestrians> is 11% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 53/200. . . Train Loss 0.1052. . . Train Acc 0.9692
Epoch 53/200. . . Train Loss 0.105. . . Train Acc 0.9693
Epoch 53/200. . . Train Loss 0.1047. . . Train Acc 0.9693
Epoch 53/200. . . Train Loss 0.1044. . . Train Acc 0.9694
Epoch 53/200. . . Train Loss 0.1042. . . Train Acc 0.9695
Epoch 53/200. . . Train Loss 0.104. . . Train Acc 0.9696

Caclulating loss & accuracy on  validation set...
Epoch 53/200. . . Val Loss 0.7531. . . Val Acc 0.9462

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 12% of total wrong
<Vehicles over 3.5 me> is 9% of total wrong
<No entry> is 10% of total wrong
<Pedestrians> is 7% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 54/200. . . Train Loss 0.1037. . . Train Acc 0.9696
Epoch 54/200. . . Train Loss 0.1035. . . Train Acc 0.9697
Epoch 54/200. . . Train Loss 0.1032. . . Train Acc 0.9698
Epoch 54/200. . . Train Loss 0.103. . . Train Acc 0.9699
Epoch 54/200. . . Train Loss 0.1027. . . Train Acc 0.9699

Caclulating loss & accuracy on  validation set...
Epoch 54/200. . . Val Loss 0.7474. . . Val Acc 0.9491

<Speed limit (20km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 5% of total wrong
<Speed limit (100km/h> is 6% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<Stop> is 7% of total wrong
<No entry> is 7% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 12% of total wrong
<Roundabout mandatory> is 7% of total wrong

Epoch 55/200. . . Train Loss 0.1025. . . Train Acc 0.97
Epoch 55/200. . . Train Loss 0.1023. . . Train Acc 0.97
Epoch 55/200. . . Train Loss 0.102. . . Train Acc 0.9701
Epoch 55/200. . . Train Loss 0.1019. . . Train Acc 0.9702
Epoch 55/200. . . Train Loss 0.1016. . . Train Acc 0.9702
Epoch 55/200. . . Train Loss 0.1014. . . Train Acc 0.9703

Caclulating loss & accuracy on  validation set...
Epoch 55/200. . . Val Loss 0.7826. . . Val Acc 0.9457

<Speed limit (70km/h)> is 13% of total wrong
<No entry> is 9% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 8% of total wrong
<Children crossing> is 9% of total wrong
<Roundabout mandatory> is 6% of total wrong

Displaying validation samples...
Epoch 56/200. . . Train Loss 0.1011. . . Train Acc 0.9704
Epoch 56/200. . . Train Loss 0.1009. . . Train Acc 0.9705
Epoch 56/200. . . Train Loss 0.1007. . . Train Acc 0.9705
Epoch 56/200. . . Train Loss 0.1004. . . Train Acc 0.9706
Epoch 56/200. . . Train Loss 0.1002. . . Train Acc 0.9707

Caclulating loss & accuracy on  validation set...
Epoch 56/200. . . Val Loss 0.8325. . . Val Acc 0.9574
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 9% of total wrong
<Vehicles over 3.5 me> is 10% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 10% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 10% of total wrong

Epoch 57/200. . . Train Loss 0.09999. . . Train Acc 0.9707
Epoch 57/200. . . Train Loss 0.0998. . . Train Acc 0.9708
Epoch 57/200. . . Train Loss 0.0996. . . Train Acc 0.9708
Epoch 57/200. . . Train Loss 0.09941. . . Train Acc 0.9709
Epoch 57/200. . . Train Loss 0.09919. . . Train Acc 0.9709
Epoch 57/200. . . Train Loss 0.099. . . Train Acc 0.971

Caclulating loss & accuracy on  validation set...
Epoch 57/200. . . Val Loss 0.8146. . . Val Acc 0.954
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 5% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 12% of total wrong
<Children crossing> is 10% of total wrong

Epoch 58/200. . . Train Loss 0.09878. . . Train Acc 0.9711
Epoch 58/200. . . Train Loss 0.09855. . . Train Acc 0.9711
Epoch 58/200. . . Train Loss 0.09833. . . Train Acc 0.9712
Epoch 58/200. . . Train Loss 0.09811. . . Train Acc 0.9713
Epoch 58/200. . . Train Loss 0.09791. . . Train Acc 0.9713

Caclulating loss & accuracy on  validation set...
Epoch 58/200. . . Val Loss 0.8834. . . Val Acc 0.9481

<Speed limit (20km/h)> is 10% of total wrong
<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<No entry> is 8% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 12% of total wrong

Epoch 59/200. . . Train Loss 0.09768. . . Train Acc 0.9714
Epoch 59/200. . . Train Loss 0.09747. . . Train Acc 0.9715
Epoch 59/200. . . Train Loss 0.09725. . . Train Acc 0.9715
Epoch 59/200. . . Train Loss 0.09703. . . Train Acc 0.9716
Epoch 59/200. . . Train Loss 0.09681. . . Train Acc 0.9716

Caclulating loss & accuracy on  validation set...
Epoch 59/200. . . Val Loss 0.9218. . . Val Acc 0.9585
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 7% of total wrong
<Bicycles crossing> is 7% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 60/200. . . Train Loss 0.09665. . . Train Acc 0.9717
Epoch 60/200. . . Train Loss 0.09648. . . Train Acc 0.9717
Epoch 60/200. . . Train Loss 0.09628. . . Train Acc 0.9718
Epoch 60/200. . . Train Loss 0.09608. . . Train Acc 0.9719
Epoch 60/200. . . Train Loss 0.0959. . . Train Acc 0.9719
Epoch 60/200. . . Train Loss 0.09573. . . Train Acc 0.972

Caclulating loss & accuracy on  validation set...
Epoch 60/200. . . Val Loss 1.161. . . Val Acc 0.9389

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (50km/h)> is 11% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 11% of total wrong
<Double curve> is 6% of total wrong
<Pedestrians> is 6% of total wrong
<Beware of ice/snow> is 12% of total wrong

Displaying validation samples...
Epoch 61/200. . . Train Loss 0.09555. . . Train Acc 0.972
Epoch 61/200. . . Train Loss 0.09534. . . Train Acc 0.9721
Epoch 61/200. . . Train Loss 0.09512. . . Train Acc 0.9722
Epoch 61/200. . . Train Loss 0.09492. . . Train Acc 0.9722
Epoch 61/200. . . Train Loss 0.0947. . . Train Acc 0.9723

Caclulating loss & accuracy on  validation set...
Epoch 61/200. . . Val Loss 1.131. . . Val Acc 0.9545
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (120km/h> is 6% of total wrong
<Right-of-way at the > is 8% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 7% of total wrong

Epoch 62/200. . . Train Loss 0.09452. . . Train Acc 0.9723
Epoch 62/200. . . Train Loss 0.09434. . . Train Acc 0.9724
Epoch 62/200. . . Train Loss 0.09414. . . Train Acc 0.9724
Epoch 62/200. . . Train Loss 0.09395. . . Train Acc 0.9725
Epoch 62/200. . . Train Loss 0.09381. . . Train Acc 0.9725
Epoch 62/200. . . Train Loss 0.09362. . . Train Acc 0.9726

Caclulating loss & accuracy on  validation set...
Epoch 62/200. . . Val Loss 0.8327. . . Val Acc 0.9565
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 10% of total wrong
<Double curve> is 8% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 10% of total wrong
<Children crossing> is 5% of total wrong
<Roundabout mandatory> is 9% of total wrong

Epoch 63/200. . . Train Loss 0.0934. . . Train Acc 0.9727
Epoch 63/200. . . Train Loss 0.09323. . . Train Acc 0.9727
Epoch 63/200. . . Train Loss 0.09306. . . Train Acc 0.9728
Epoch 63/200. . . Train Loss 0.09284. . . Train Acc 0.9728
Epoch 63/200. . . Train Loss 0.09265. . . Train Acc 0.9729

Caclulating loss & accuracy on  validation set...
Epoch 63/200. . . Val Loss 1.373. . . Val Acc 0.9467

<No entry> is 11% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 9% of total wrong
<Slippery road> is 13% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 6% of total wrong
<Beware of ice/snow> is 9% of total wrong

Epoch 64/200. . . Train Loss 0.09247. . . Train Acc 0.9729
Epoch 64/200. . . Train Loss 0.09229. . . Train Acc 0.973
Epoch 64/200. . . Train Loss 0.09211. . . Train Acc 0.973
Epoch 64/200. . . Train Loss 0.09196. . . Train Acc 0.9731
Epoch 64/200. . . Train Loss 0.09177. . . Train Acc 0.9731
Epoch 64/200. . . Train Loss 0.09159. . . Train Acc 0.9732

Caclulating loss & accuracy on  validation set...
Epoch 64/200. . . Val Loss 1.511. . . Val Acc 0.9512
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (70km/h)> is 14% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Double curve> is 6% of total wrong
<Road narrows on the > is 11% of total wrong
<Road work> is 8% of total wrong
<Pedestrians> is 10% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 65/200. . . Train Loss 0.09142. . . Train Acc 0.9732
Epoch 65/200. . . Train Loss 0.09125. . . Train Acc 0.9733
Epoch 65/200. . . Train Loss 0.0911. . . Train Acc 0.9733
Epoch 65/200. . . Train Loss 0.09091. . . Train Acc 0.9734
Epoch 65/200. . . Train Loss 0.09073. . . Train Acc 0.9734

Caclulating loss & accuracy on  validation set...
Epoch 65/200. . . Val Loss 0.8566. . . Val Acc 0.9536
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (70km/h)> is 8% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (100km/h> is 7% of total wrong
<Speed limit (120km/h> is 16% of total wrong
<No entry> is 8% of total wrong
<Road work> is 8% of total wrong
<Roundabout mandatory> is 8% of total wrong

Displaying validation samples...
Epoch 66/200. . . Train Loss 0.09055. . . Train Acc 0.9735
Epoch 66/200. . . Train Loss 0.09039. . . Train Acc 0.9735
Epoch 66/200. . . Train Loss 0.09022. . . Train Acc 0.9736
Epoch 66/200. . . Train Loss 0.09006. . . Train Acc 0.9736
Epoch 66/200. . . Train Loss 0.08989. . . Train Acc 0.9737
Epoch 66/200. . . Train Loss 0.0897. . . Train Acc 0.9737

Caclulating loss & accuracy on  validation set...
Epoch 66/200. . . Val Loss 0.9591. . . Val Acc 0.9538
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 10% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 15% of total wrong
<No entry> is 7% of total wrong
<Dangerous curve to t> is 5% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 7% of total wrong
<Pedestrians> is 6% of total wrong

Epoch 67/200. . . Train Loss 0.08956. . . Train Acc 0.9738
Epoch 67/200. . . Train Loss 0.08941. . . Train Acc 0.9738
Epoch 67/200. . . Train Loss 0.08924. . . Train Acc 0.9739
Epoch 67/200. . . Train Loss 0.08906. . . Train Acc 0.9739
Epoch 67/200. . . Train Loss 0.08892. . . Train Acc 0.974

Caclulating loss & accuracy on  validation set...
Epoch 67/200. . . Val Loss 1.647. . . Val Acc 0.9581
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 6% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Double curve> is 5% of total wrong
<Road narrows on the > is 7% of total wrong
<Road work> is 10% of total wrong

Epoch 68/200. . . Train Loss 0.08878. . . Train Acc 0.974
Epoch 68/200. . . Train Loss 0.08862. . . Train Acc 0.9741
Epoch 68/200. . . Train Loss 0.08845. . . Train Acc 0.9741
Epoch 68/200. . . Train Loss 0.08829. . . Train Acc 0.9741
Epoch 68/200. . . Train Loss 0.08812. . . Train Acc 0.9742

Caclulating loss & accuracy on  validation set...
Epoch 68/200. . . Val Loss 1.586. . . Val Acc 0.9659
Model Saved!

<Speed limit (30km/h)> is 5% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (120km/h> is 9% of total wrong
<No entry> is 20% of total wrong
<Dangerous curve to t> is 13% of total wrong
<Road work> is 7% of total wrong
<Traffic signals> is 5% of total wrong
<Pedestrians> is 8% of total wrong

Epoch 69/200. . . Train Loss 0.08799. . . Train Acc 0.9742
Epoch 69/200. . . Train Loss 0.08782. . . Train Acc 0.9743
Epoch 69/200. . . Train Loss 0.08766. . . Train Acc 0.9743
Epoch 69/200. . . Train Loss 0.08751. . . Train Acc 0.9744
Epoch 69/200. . . Train Loss 0.08735. . . Train Acc 0.9744
Epoch 69/200. . . Train Loss 0.08719. . . Train Acc 0.9745

Caclulating loss & accuracy on  validation set...
Epoch 69/200. . . Val Loss 1.239. . . Val Acc 0.9596
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (20km/h)> is 9% of total wrong
<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<Vehicles over 3.5 me> is 9% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 17% of total wrong
<Pedestrians> is 5% of total wrong

Epoch 70/200. . . Train Loss 0.08704. . . Train Acc 0.9745
Epoch 70/200. . . Train Loss 0.08686. . . Train Acc 0.9746
Epoch 70/200. . . Train Loss 0.08671. . . Train Acc 0.9746
Epoch 70/200. . . Train Loss 0.08654. . . Train Acc 0.9747
Epoch 70/200. . . Train Loss 0.08636. . . Train Acc 0.9747

Caclulating loss & accuracy on  validation set...
Epoch 70/200. . . Val Loss 1.401. . . Val Acc 0.9459

<Speed limit (30km/h)> is 13% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 18% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 7% of total wrong

Displaying validation samples...
Epoch 71/200. . . Train Loss 0.08622. . . Train Acc 0.9748
Epoch 71/200. . . Train Loss 0.08605. . . Train Acc 0.9748
Epoch 71/200. . . Train Loss 0.08589. . . Train Acc 0.9749
Epoch 71/200. . . Train Loss 0.08575. . . Train Acc 0.9749
Epoch 71/200. . . Train Loss 0.08563. . . Train Acc 0.9749
Epoch 71/200. . . Train Loss 0.0855. . . Train Acc 0.975

Caclulating loss & accuracy on  validation set...
Epoch 71/200. . . Val Loss 2.377. . . Val Acc 0.9471

<Speed limit (30km/h)> is 7% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 16% of total wrong
<Pedestrians> is 7% of total wrong
<Turn right ahead> is 9% of total wrong
<Roundabout mandatory> is 7% of total wrong

Epoch 72/200. . . Train Loss 0.08536. . . Train Acc 0.975
Epoch 72/200. . . Train Loss 0.08521. . . Train Acc 0.9751
Epoch 72/200. . . Train Loss 0.08508. . . Train Acc 0.9751
Epoch 72/200. . . Train Loss 0.08492. . . Train Acc 0.9751
Epoch 72/200. . . Train Loss 0.08478. . . Train Acc 0.9752

Caclulating loss & accuracy on  validation set...
Epoch 72/200. . . Val Loss 1.204. . . Val Acc 0.9623
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 7% of total wrong
<Roundabout mandatory> is 10% of total wrong

Epoch 73/200. . . Train Loss 0.08466. . . Train Acc 0.9752
Epoch 73/200. . . Train Loss 0.0845. . . Train Acc 0.9753
Epoch 73/200. . . Train Loss 0.08437. . . Train Acc 0.9753
Epoch 73/200. . . Train Loss 0.08422. . . Train Acc 0.9753
Epoch 73/200. . . Train Loss 0.08408. . . Train Acc 0.9754
Epoch 73/200. . . Train Loss 0.08393. . . Train Acc 0.9754

Caclulating loss & accuracy on  validation set...
Epoch 73/200. . . Val Loss 1.56. . . Val Acc 0.9612
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (70km/h)> is 6% of total wrong
<Right-of-way at the > is 5% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Double curve> is 8% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 6% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 74/200. . . Train Loss 0.08379. . . Train Acc 0.9755
Epoch 74/200. . . Train Loss 0.08366. . . Train Acc 0.9755
Epoch 74/200. . . Train Loss 0.08351. . . Train Acc 0.9755
Epoch 74/200. . . Train Loss 0.08338. . . Train Acc 0.9756
Epoch 74/200. . . Train Loss 0.08327. . . Train Acc 0.9756

Caclulating loss & accuracy on  validation set...
Epoch 74/200. . . Val Loss 2.604. . . Val Acc 0.9515
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 13% of total wrong
<Speed limit (70km/h)> is 5% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 13% of total wrong
<Double curve> is 10% of total wrong
<Road work> is 10% of total wrong

Epoch 75/200. . . Train Loss 0.08311. . . Train Acc 0.9757
Epoch 75/200. . . Train Loss 0.08296. . . Train Acc 0.9757
Epoch 75/200. . . Train Loss 0.08282. . . Train Acc 0.9757
Epoch 75/200. . . Train Loss 0.08268. . . Train Acc 0.9758
Epoch 75/200. . . Train Loss 0.08255. . . Train Acc 0.9758
Epoch 75/200. . . Train Loss 0.08244. . . Train Acc 0.9759

Caclulating loss & accuracy on  validation set...
Epoch 75/200. . . Val Loss 3.021. . . Val Acc 0.9368

<Speed limit (30km/h)> is 5% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<Right-of-way at the > is 8% of total wrong
<Stop> is 12% of total wrong
<Vehicles over 3.5 me> is 10% of total wrong
<No entry> is 8% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 7% of total wrong

Displaying validation samples...
Epoch 76/200. . . Train Loss 0.08231. . . Train Acc 0.9759
Epoch 76/200. . . Train Loss 0.08219. . . Train Acc 0.9759
Epoch 76/200. . . Train Loss 0.08206. . . Train Acc 0.976
Epoch 76/200. . . Train Loss 0.08192. . . Train Acc 0.976
Epoch 76/200. . . Train Loss 0.08179. . . Train Acc 0.9761

Caclulating loss & accuracy on  validation set...
Epoch 76/200. . . Val Loss 1.927. . . Val Acc 0.9688
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 9% of total wrong
<No entry> is 20% of total wrong
<Dangerous curve to t> is 14% of total wrong
<Double curve> is 5% of total wrong
<Pedestrians> is 5% of total wrong
<Go straight or left> is 5% of total wrong
<Roundabout mandatory> is 9% of total wrong

Epoch 77/200. . . Train Loss 0.08166. . . Train Acc 0.9761
Epoch 77/200. . . Train Loss 0.08153. . . Train Acc 0.9761
Epoch 77/200. . . Train Loss 0.0814. . . Train Acc 0.9762
Epoch 77/200. . . Train Loss 0.08129. . . Train Acc 0.9762
Epoch 77/200. . . Train Loss 0.08119. . . Train Acc 0.9762

Caclulating loss & accuracy on  validation set...
Epoch 77/200. . . Val Loss 1.532. . . Val Acc 0.9588
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 9% of total wrong
<Vehicles over 3.5 me> is 15% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Road work> is 13% of total wrong

Epoch 78/200. . . Train Loss 0.08108. . . Train Acc 0.9763
Epoch 78/200. . . Train Loss 0.08094. . . Train Acc 0.9763
Epoch 78/200. . . Train Loss 0.08082. . . Train Acc 0.9763
Epoch 78/200. . . Train Loss 0.08068. . . Train Acc 0.9764
Epoch 78/200. . . Train Loss 0.08057. . . Train Acc 0.9764
Epoch 78/200. . . Train Loss 0.08043. . . Train Acc 0.9765

Caclulating loss & accuracy on  validation set...
Epoch 78/200. . . Val Loss 1.269. . . Val Acc 0.9636
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (70km/h)> is 7% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<Vehicles over 3.5 me> is 6% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 7% of total wrong

Epoch 79/200. . . Train Loss 0.08032. . . Train Acc 0.9765
Epoch 79/200. . . Train Loss 0.08019. . . Train Acc 0.9765
Epoch 79/200. . . Train Loss 0.08005. . . Train Acc 0.9766
Epoch 79/200. . . Train Loss 0.07992. . . Train Acc 0.9766
Epoch 79/200. . . Train Loss 0.07978. . . Train Acc 0.9767

Caclulating loss & accuracy on  validation set...
Epoch 79/200. . . Val Loss 1.758. . . Val Acc 0.9534
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 5% of total wrong
<Vehicles over 3.5 me> is 14% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 10% of total wrong

Epoch 80/200. . . Train Loss 0.07968. . . Train Acc 0.9767
Epoch 80/200. . . Train Loss 0.07956. . . Train Acc 0.9767
Epoch 80/200. . . Train Loss 0.07943. . . Train Acc 0.9768
Epoch 80/200. . . Train Loss 0.0793. . . Train Acc 0.9768
Epoch 80/200. . . Train Loss 0.07918. . . Train Acc 0.9768
Epoch 80/200. . . Train Loss 0.07906. . . Train Acc 0.9769

Caclulating loss & accuracy on  validation set...
Epoch 80/200. . . Val Loss 1.69. . . Val Acc 0.9638
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 8% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<Vehicles over 3.5 me> is 12% of total wrong
<No entry> is 18% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 7% of total wrong
<Pedestrians> is 6% of total wrong

Displaying validation samples...
Epoch 81/200. . . Train Loss 0.07893. . . Train Acc 0.9769
Epoch 81/200. . . Train Loss 0.07882. . . Train Acc 0.9769
Epoch 81/200. . . Train Loss 0.07873. . . Train Acc 0.977
Epoch 81/200. . . Train Loss 0.07863. . . Train Acc 0.977
Epoch 81/200. . . Train Loss 0.07851. . . Train Acc 0.977

Caclulating loss & accuracy on  validation set...
Epoch 81/200. . . Val Loss 1.574. . . Val Acc 0.9584
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<Right-of-way at the > is 6% of total wrong
<Vehicles over 3.5 me> is 10% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 13% of total wrong

Epoch 82/200. . . Train Loss 0.07839. . . Train Acc 0.9771
Epoch 82/200. . . Train Loss 0.07826. . . Train Acc 0.9771
Epoch 82/200. . . Train Loss 0.07814. . . Train Acc 0.9771
Epoch 82/200. . . Train Loss 0.07802. . . Train Acc 0.9772
Epoch 82/200. . . Train Loss 0.07789. . . Train Acc 0.9772
Epoch 82/200. . . Train Loss 0.07777. . . Train Acc 0.9772

Caclulating loss & accuracy on  validation set...
Epoch 82/200. . . Val Loss 3.454. . . Val Acc 0.9354

<Speed limit (20km/h)> is 9% of total wrong
<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (60km/h)> is 7% of total wrong
<Speed limit (120km/h> is 18% of total wrong
<No entry> is 9% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 5% of total wrong
<Children crossing> is 6% of total wrong

Epoch 83/200. . . Train Loss 0.07766. . . Train Acc 0.9773
Epoch 83/200. . . Train Loss 0.07753. . . Train Acc 0.9773
Epoch 83/200. . . Train Loss 0.07742. . . Train Acc 0.9773
Epoch 83/200. . . Train Loss 0.0773. . . Train Acc 0.9774
Epoch 83/200. . . Train Loss 0.0772. . . Train Acc 0.9774

Caclulating loss & accuracy on  validation set...
Epoch 83/200. . . Val Loss 2.459. . . Val Acc 0.9598
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (20km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<No passing> is 6% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 8% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 84/200. . . Train Loss 0.07709. . . Train Acc 0.9774
Epoch 84/200. . . Train Loss 0.07697. . . Train Acc 0.9775
Epoch 84/200. . . Train Loss 0.07686. . . Train Acc 0.9775
Epoch 84/200. . . Train Loss 0.07674. . . Train Acc 0.9775
Epoch 84/200. . . Train Loss 0.07661. . . Train Acc 0.9776

Caclulating loss & accuracy on  validation set...
Epoch 84/200. . . Val Loss 1.892. . . Val Acc 0.967
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 11% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 10% of total wrong
<Children crossing> is 7% of total wrong

Epoch 85/200. . . Train Loss 0.0765. . . Train Acc 0.9776
Epoch 85/200. . . Train Loss 0.07639. . . Train Acc 0.9776
Epoch 85/200. . . Train Loss 0.07629. . . Train Acc 0.9777
Epoch 85/200. . . Train Loss 0.07618. . . Train Acc 0.9777
Epoch 85/200. . . Train Loss 0.07609. . . Train Acc 0.9777
Epoch 85/200. . . Train Loss 0.076. . . Train Acc 0.9777

Caclulating loss & accuracy on  validation set...
Epoch 85/200. . . Val Loss 1.918. . . Val Acc 0.9707
Model Saved!

<Speed limit (20km/h)> is 7% of total wrong
<Speed limit (30km/h)> is 12% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 10% of total wrong

Displaying validation samples...
Epoch 86/200. . . Train Loss 0.0759. . . Train Acc 0.9778
Epoch 86/200. . . Train Loss 0.0758. . . Train Acc 0.9778
Epoch 86/200. . . Train Loss 0.07568. . . Train Acc 0.9778
Epoch 86/200. . . Train Loss 0.07557. . . Train Acc 0.9779
Epoch 86/200. . . Train Loss 0.07547. . . Train Acc 0.9779

Caclulating loss & accuracy on  validation set...
Epoch 86/200. . . Val Loss 2.311. . . Val Acc 0.9643
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (20km/h)> is 10% of total wrong
<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (50km/h)> is 8% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 15% of total wrong
<Pedestrians> is 13% of total wrong

Epoch 87/200. . . Train Loss 0.07537. . . Train Acc 0.9779
Epoch 87/200. . . Train Loss 0.07526. . . Train Acc 0.978
Epoch 87/200. . . Train Loss 0.07516. . . Train Acc 0.978
Epoch 87/200. . . Train Loss 0.07506. . . Train Acc 0.978
Epoch 87/200. . . Train Loss 0.07496. . . Train Acc 0.9781
Epoch 87/200. . . Train Loss 0.07484. . . Train Acc 0.9781

Caclulating loss & accuracy on  validation set...
Epoch 87/200. . . Val Loss 2.805. . . Val Acc 0.9569
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (20km/h)> is 7% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Double curve> is 14% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 6% of total wrong
<Children crossing> is 7% of total wrong

Epoch 88/200. . . Train Loss 0.07473. . . Train Acc 0.9781
Epoch 88/200. . . Train Loss 0.07463. . . Train Acc 0.9782
Epoch 88/200. . . Train Loss 0.07453. . . Train Acc 0.9782
Epoch 88/200. . . Train Loss 0.07443. . . Train Acc 0.9782
Epoch 88/200. . . Train Loss 0.0743. . . Train Acc 0.9782

Caclulating loss & accuracy on  validation set...
Epoch 88/200. . . Val Loss 3.307. . . Val Acc 0.9535
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (60km/h)> is 21% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 12% of total wrong

Epoch 89/200. . . Train Loss 0.07421. . . Train Acc 0.9783
Epoch 89/200. . . Train Loss 0.0741. . . Train Acc 0.9783
Epoch 89/200. . . Train Loss 0.074. . . Train Acc 0.9783
Epoch 89/200. . . Train Loss 0.07389. . . Train Acc 0.9784
Epoch 89/200. . . Train Loss 0.07379. . . Train Acc 0.9784
Epoch 89/200. . . Train Loss 0.07369. . . Train Acc 0.9784

Caclulating loss & accuracy on  validation set...
Epoch 89/200. . . Val Loss 2.534. . . Val Acc 0.9726
mkdir: cannot create directory ‘checks_97’: File exists
Model Saved!

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (50km/h)> is 9% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No passing for vehic> is 12% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 7% of total wrong

Epoch 90/200. . . Train Loss 0.0735. . . Train Acc 0.9785
Epoch 90/200. . . Train Loss 0.0734. . . Train Acc 0.9785
Epoch 90/200. . . Train Loss 0.0733. . . Train Acc 0.9785
Epoch 90/200. . . Train Loss 0.0732. . . Train Acc 0.9786

Caclulating loss & accuracy on  validation set...
Epoch 90/200. . . Val Loss 3.074. . . Val Acc 0.9676
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<No entry> is 16% of total wrong
<Dangerous curve to t> is 14% of total wrong
<Road work> is 6% of total wrong
<Children crossing> is 10% of total wrong
<Ahead only> is 8% of total wrong

Displaying validation samples...
Epoch 91/200. . . Train Loss 0.0731. . . Train Acc 0.9786
Epoch 91/200. . . Train Loss 0.07301. . . Train Acc 0.9786
Epoch 91/200. . . Train Loss 0.07292. . . Train Acc 0.9786
Epoch 91/200. . . Train Loss 0.07283. . . Train Acc 0.9787
Epoch 91/200. . . Train Loss 0.07274. . . Train Acc 0.9787
Epoch 91/200. . . Train Loss 0.07265. . . Train Acc 0.9787

Caclulating loss & accuracy on  validation set...
Epoch 91/200. . . Val Loss 2.64. . . Val Acc 0.9639
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (120km/h> is 14% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 20% of total wrong
<Double curve> is 9% of total wrong
<Turn right ahead> is 6% of total wrong

Epoch 92/200. . . Train Loss 0.07257. . . Train Acc 0.9787
Epoch 92/200. . . Train Loss 0.07246. . . Train Acc 0.9788
Epoch 92/200. . . Train Loss 0.07237. . . Train Acc 0.9788
Epoch 92/200. . . Train Loss 0.07229. . . Train Acc 0.9788
Epoch 92/200. . . Train Loss 0.07219. . . Train Acc 0.9789

Caclulating loss & accuracy on  validation set...
Epoch 92/200. . . Val Loss 2.912. . . Val Acc 0.9683
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 14% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 6% of total wrong
<Pedestrians> is 5% of total wrong
<Children crossing> is 6% of total wrong

Epoch 93/200. . . Train Loss 0.07208. . . Train Acc 0.9789
Epoch 93/200. . . Train Loss 0.07198. . . Train Acc 0.9789
Epoch 93/200. . . Train Loss 0.07188. . . Train Acc 0.9789
Epoch 93/200. . . Train Loss 0.07177. . . Train Acc 0.979
Epoch 93/200. . . Train Loss 0.07169. . . Train Acc 0.979

Caclulating loss & accuracy on  validation set...
Epoch 93/200. . . Val Loss 3.203. . . Val Acc 0.9693
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<No entry> is 20% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Double curve> is 8% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 5% of total wrong
<Beware of ice/snow> is 5% of total wrong

Epoch 94/200. . . Train Loss 0.0716. . . Train Acc 0.979
Epoch 94/200. . . Train Loss 0.07151. . . Train Acc 0.9791
Epoch 94/200. . . Train Loss 0.07141. . . Train Acc 0.9791
Epoch 94/200. . . Train Loss 0.07132. . . Train Acc 0.9791
Epoch 94/200. . . Train Loss 0.07123. . . Train Acc 0.9791
Epoch 94/200. . . Train Loss 0.07114. . . Train Acc 0.9792

Caclulating loss & accuracy on  validation set...
Epoch 94/200. . . Val Loss 3.606. . . Val Acc 0.9659
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<No entry> is 15% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 16% of total wrong
<Turn right ahead> is 6% of total wrong

Epoch 95/200. . . Train Loss 0.07105. . . Train Acc 0.9792
Epoch 95/200. . . Train Loss 0.07097. . . Train Acc 0.9792
Epoch 95/200. . . Train Loss 0.07088. . . Train Acc 0.9792
Epoch 95/200. . . Train Loss 0.07078. . . Train Acc 0.9793
Epoch 95/200. . . Train Loss 0.07069. . . Train Acc 0.9793

Caclulating loss & accuracy on  validation set...
Epoch 95/200. . . Val Loss 3.224. . . Val Acc 0.9665
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 5% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Double curve> is 9% of total wrong
<Pedestrians> is 5% of total wrong

Displaying validation samples...
Epoch 96/200. . . Train Loss 0.07061. . . Train Acc 0.9793
Epoch 96/200. . . Train Loss 0.07051. . . Train Acc 0.9793
Epoch 96/200. . . Train Loss 0.07043. . . Train Acc 0.9794
Epoch 96/200. . . Train Loss 0.07035. . . Train Acc 0.9794
Epoch 96/200. . . Train Loss 0.07025. . . Train Acc 0.9794
Epoch 96/200. . . Train Loss 0.07015. . . Train Acc 0.9795

Caclulating loss & accuracy on  validation set...
Epoch 96/200. . . Val Loss 3.316. . . Val Acc 0.9668
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (20km/h)> is 6% of total wrong
<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (100km/h> is 8% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 10% of total wrong

Epoch 97/200. . . Train Loss 0.07005. . . Train Acc 0.9795
Epoch 97/200. . . Train Loss 0.06996. . . Train Acc 0.9795
Epoch 97/200. . . Train Loss 0.06987. . . Train Acc 0.9795
Epoch 97/200. . . Train Loss 0.06977. . . Train Acc 0.9796
Epoch 97/200. . . Train Loss 0.06969. . . Train Acc 0.9796

Caclulating loss & accuracy on  validation set...
Epoch 97/200. . . Val Loss 4.724. . . Val Acc 0.9658
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Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 9% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Double curve> is 7% of total wrong
<Children crossing> is 5% of total wrong
<Turn left ahead> is 7% of total wrong

Epoch 98/200. . . Train Loss 0.06962. . . Train Acc 0.9796
Epoch 98/200. . . Train Loss 0.06954. . . Train Acc 0.9796
Epoch 98/200. . . Train Loss 0.06945. . . Train Acc 0.9797
Epoch 98/200. . . Train Loss 0.06937. . . Train Acc 0.9797
Epoch 98/200. . . Train Loss 0.06929. . . Train Acc 0.9797
Epoch 98/200. . . Train Loss 0.06921. . . Train Acc 0.9797

Caclulating loss & accuracy on  validation set...
Epoch 98/200. . . Val Loss 3.804. . . Val Acc 0.9605
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (20km/h)> is 6% of total wrong
<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 7% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 6% of total wrong
<Children crossing> is 12% of total wrong
<Bicycles crossing> is 9% of total wrong

Epoch 99/200. . . Train Loss 0.06912. . . Train Acc 0.9798
Epoch 99/200. . . Train Loss 0.06904. . . Train Acc 0.9798
Epoch 99/200. . . Train Loss 0.06896. . . Train Acc 0.9798
Epoch 99/200. . . Train Loss 0.06886. . . Train Acc 0.9798
Epoch 99/200. . . Train Loss 0.06877. . . Train Acc 0.9799

Caclulating loss & accuracy on  validation set...
Epoch 99/200. . . Val Loss 4.637. . . Val Acc 0.9655
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (20km/h)> is 9% of total wrong
<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 7% of total wrong

Epoch 100/200. . . Train Loss 0.06869. . . Train Acc 0.9799
Epoch 100/200. . . Train Loss 0.06861. . . Train Acc 0.9799
Epoch 100/200. . . Train Loss 0.06852. . . Train Acc 0.9799
Epoch 100/200. . . Train Loss 0.06844. . . Train Acc 0.98
Epoch 100/200. . . Train Loss 0.06835. . . Train Acc 0.98
Epoch 100/200. . . Train Loss 0.06828. . . Train Acc 0.98

Caclulating loss & accuracy on  validation set...
Epoch 100/200. . . Val Loss 3.112. . . Val Acc 0.9737
mkdir: cannot create directory ‘checks_97’: File exists
Model Saved!

<Speed limit (80km/h)> is 7% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Double curve> is 11% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 8% of total wrong

Displaying validation samples...
Epoch 101/200. . . Train Loss 0.06819. . . Train Acc 0.98
Epoch 101/200. . . Train Loss 0.0681. . . Train Acc 0.9801
Epoch 101/200. . . Train Loss 0.06804. . . Train Acc 0.9801
Epoch 101/200. . . Train Loss 0.06797. . . Train Acc 0.9801
Epoch 101/200. . . Train Loss 0.06789. . . Train Acc 0.9801

Caclulating loss & accuracy on  validation set...
Epoch 101/200. . . Val Loss 3.483. . . Val Acc 0.9697
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 9% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 15% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 18% of total wrong
<Pedestrians> is 10% of total wrong

Epoch 102/200. . . Train Loss 0.06781. . . Train Acc 0.9801
Epoch 102/200. . . Train Loss 0.06774. . . Train Acc 0.9802
Epoch 102/200. . . Train Loss 0.06767. . . Train Acc 0.9802
Epoch 102/200. . . Train Loss 0.0676. . . Train Acc 0.9802
Epoch 102/200. . . Train Loss 0.06751. . . Train Acc 0.9802

Caclulating loss & accuracy on  validation set...
Epoch 102/200. . . Val Loss 4.935. . . Val Acc 0.9659
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 5% of total wrong
<No entry> is 21% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Double curve> is 10% of total wrong
<Road work> is 8% of total wrong
<Pedestrians> is 12% of total wrong

Epoch 103/200. . . Train Loss 0.06743. . . Train Acc 0.9803
Epoch 103/200. . . Train Loss 0.06736. . . Train Acc 0.9803
Epoch 103/200. . . Train Loss 0.06729. . . Train Acc 0.9803
Epoch 103/200. . . Train Loss 0.06721. . . Train Acc 0.9803
Epoch 103/200. . . Train Loss 0.06714. . . Train Acc 0.9803
Epoch 103/200. . . Train Loss 0.06705. . . Train Acc 0.9804

Caclulating loss & accuracy on  validation set...
Epoch 103/200. . . Val Loss 4.371. . . Val Acc 0.9684
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 8% of total wrong
<Yield> is 6% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 6% of total wrong
<Wild animals crossin> is 9% of total wrong

Epoch 104/200. . . Train Loss 0.06696. . . Train Acc 0.9804
Epoch 104/200. . . Train Loss 0.06688. . . Train Acc 0.9804
Epoch 104/200. . . Train Loss 0.0668. . . Train Acc 0.9804
Epoch 104/200. . . Train Loss 0.06672. . . Train Acc 0.9805
Epoch 104/200. . . Train Loss 0.06665. . . Train Acc 0.9805

Caclulating loss & accuracy on  validation set...
Epoch 104/200. . . Val Loss 3.978. . . Val Acc 0.9633
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (20km/h)> is 16% of total wrong
<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 18% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 9% of total wrong
<Beware of ice/snow> is 6% of total wrong

Epoch 105/200. . . Train Loss 0.06657. . . Train Acc 0.9805
Epoch 105/200. . . Train Loss 0.0665. . . Train Acc 0.9805
Epoch 105/200. . . Train Loss 0.06642. . . Train Acc 0.9806
Epoch 105/200. . . Train Loss 0.06634. . . Train Acc 0.9806
Epoch 105/200. . . Train Loss 0.06627. . . Train Acc 0.9806
Epoch 105/200. . . Train Loss 0.06621. . . Train Acc 0.9806

Caclulating loss & accuracy on  validation set...
Epoch 105/200. . . Val Loss 3.43. . . Val Acc 0.9757
mkdir: cannot create directory ‘checks_97’: File exists
Model Saved!

<No entry> is 23% of total wrong
<Dangerous curve to t> is 11% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 6% of total wrong

Displaying validation samples...
Epoch 106/200. . . Train Loss 0.06613. . . Train Acc 0.9806
Epoch 106/200. . . Train Loss 0.06605. . . Train Acc 0.9807
Epoch 106/200. . . Train Loss 0.06596. . . Train Acc 0.9807
Epoch 106/200. . . Train Loss 0.0659. . . Train Acc 0.9807
Epoch 106/200. . . Train Loss 0.06583. . . Train Acc 0.9807

Caclulating loss & accuracy on  validation set...
Epoch 106/200. . . Val Loss 4.649. . . Val Acc 0.971
mkdir: cannot create directory ‘checks_97’: File exists
Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<No entry> is 23% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 6% of total wrong
<Pedestrians> is 9% of total wrong

Epoch 107/200. . . Train Loss 0.06575. . . Train Acc 0.9807
Epoch 107/200. . . Train Loss 0.06566. . . Train Acc 0.9808
Epoch 107/200. . . Train Loss 0.06559. . . Train Acc 0.9808
Epoch 107/200. . . Train Loss 0.06553. . . Train Acc 0.9808
Epoch 107/200. . . Train Loss 0.06546. . . Train Acc 0.9808
Epoch 107/200. . . Train Loss 0.06539. . . Train Acc 0.9809

Caclulating loss & accuracy on  validation set...
Epoch 107/200. . . Val Loss 5.189. . . Val Acc 0.9624
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Road narrows on the > is 10% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 5% of total wrong
<Children crossing> is 8% of total wrong

Epoch 108/200. . . Train Loss 0.06533. . . Train Acc 0.9809
Epoch 108/200. . . Train Loss 0.06525. . . Train Acc 0.9809
Epoch 108/200. . . Train Loss 0.06518. . . Train Acc 0.9809
Epoch 108/200. . . Train Loss 0.0651. . . Train Acc 0.9809
Epoch 108/200. . . Train Loss 0.06503. . . Train Acc 0.981

Caclulating loss & accuracy on  validation set...
Epoch 108/200. . . Val Loss 4.824. . . Val Acc 0.9551
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 9% of total wrong
<Speed limit (120km/h> is 9% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 13% of total wrong
<Children crossing> is 6% of total wrong

Epoch 109/200. . . Train Loss 0.06496. . . Train Acc 0.981
Epoch 109/200. . . Train Loss 0.0649. . . Train Acc 0.981
Epoch 109/200. . . Train Loss 0.06484. . . Train Acc 0.981
Epoch 109/200. . . Train Loss 0.06476. . . Train Acc 0.981
Epoch 109/200. . . Train Loss 0.06469. . . Train Acc 0.9811

Caclulating loss & accuracy on  validation set...
Epoch 109/200. . . Val Loss 4.915. . . Val Acc 0.944

<Speed limit (20km/h)> is 8% of total wrong
<Speed limit (80km/h)> is 11% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Road work> is 21% of total wrong
<Pedestrians> is 7% of total wrong
<Children crossing> is 7% of total wrong

Epoch 110/200. . . Train Loss 0.06462. . . Train Acc 0.9811
Epoch 110/200. . . Train Loss 0.06454. . . Train Acc 0.9811
Epoch 110/200. . . Train Loss 0.06447. . . Train Acc 0.9811
Epoch 110/200. . . Train Loss 0.0644. . . Train Acc 0.9811
Epoch 110/200. . . Train Loss 0.06433. . . Train Acc 0.9812
Epoch 110/200. . . Train Loss 0.06426. . . Train Acc 0.9812

Caclulating loss & accuracy on  validation set...
Epoch 110/200. . . Val Loss 6.359. . . Val Acc 0.9606
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 8% of total wrong
<Children crossing> is 5% of total wrong

Displaying validation samples...
Epoch 111/200. . . Train Loss 0.0642. . . Train Acc 0.9812
Epoch 111/200. . . Train Loss 0.06414. . . Train Acc 0.9812
Epoch 111/200. . . Train Loss 0.06408. . . Train Acc 0.9812
Epoch 111/200. . . Train Loss 0.064. . . Train Acc 0.9813
Epoch 111/200. . . Train Loss 0.06393. . . Train Acc 0.9813

Caclulating loss & accuracy on  validation set...
Epoch 111/200. . . Val Loss 5.379. . . Val Acc 0.9662
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 9% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 18% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 15% of total wrong
<Pedestrians> is 9% of total wrong
<Roundabout mandatory> is 9% of total wrong

Epoch 112/200. . . Train Loss 0.06385. . . Train Acc 0.9813
Epoch 112/200. . . Train Loss 0.06377. . . Train Acc 0.9813
Epoch 112/200. . . Train Loss 0.06372. . . Train Acc 0.9813
Epoch 112/200. . . Train Loss 0.06365. . . Train Acc 0.9814
Epoch 112/200. . . Train Loss 0.06359. . . Train Acc 0.9814
Epoch 112/200. . . Train Loss 0.06353. . . Train Acc 0.9814

Caclulating loss & accuracy on  validation set...
Epoch 112/200. . . Val Loss 9.92. . . Val Acc 0.9348

<Speed limit (120km/h> is 7% of total wrong
<No entry> is 8% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 9% of total wrong
<Children crossing> is 11% of total wrong
<Keep left> is 9% of total wrong
<Roundabout mandatory> is 8% of total wrong

Epoch 113/200. . . Train Loss 0.06348. . . Train Acc 0.9814
Epoch 113/200. . . Train Loss 0.06341. . . Train Acc 0.9814
Epoch 113/200. . . Train Loss 0.06334. . . Train Acc 0.9815
Epoch 113/200. . . Train Loss 0.06328. . . Train Acc 0.9815
Epoch 113/200. . . Train Loss 0.06321. . . Train Acc 0.9815

Caclulating loss & accuracy on  validation set...
Epoch 113/200. . . Val Loss 7.213. . . Val Acc 0.9605
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 19% of total wrong
<Pedestrians> is 15% of total wrong
<Children crossing> is 6% of total wrong
<Roundabout mandatory> is 7% of total wrong

Epoch 114/200. . . Train Loss 0.06314. . . Train Acc 0.9815
Epoch 114/200. . . Train Loss 0.06308. . . Train Acc 0.9815
Epoch 114/200. . . Train Loss 0.06301. . . Train Acc 0.9816
Epoch 114/200. . . Train Loss 0.06294. . . Train Acc 0.9816
Epoch 114/200. . . Train Loss 0.06287. . . Train Acc 0.9816
Epoch 114/200. . . Train Loss 0.06281. . . Train Acc 0.9816

Caclulating loss & accuracy on  validation set...
Epoch 114/200. . . Val Loss 10.33. . . Val Acc 0.9499

<Speed limit (20km/h)> is 11% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 7% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<Vehicles over 3.5 me> is 5% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 7% of total wrong
<Children crossing> is 7% of total wrong
<Roundabout mandatory> is 7% of total wrong

Epoch 115/200. . . Train Loss 0.06275. . . Train Acc 0.9816
Epoch 115/200. . . Train Loss 0.06268. . . Train Acc 0.9816
Epoch 115/200. . . Train Loss 0.06262. . . Train Acc 0.9817
Epoch 115/200. . . Train Loss 0.06254. . . Train Acc 0.9817
Epoch 115/200. . . Train Loss 0.06247. . . Train Acc 0.9817

Caclulating loss & accuracy on  validation set...
Epoch 115/200. . . Val Loss 5.584. . . Val Acc 0.9634
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (120km/h> is 12% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 14% of total wrong
<Roundabout mandatory> is 8% of total wrong

Displaying validation samples...
Epoch 116/200. . . Train Loss 0.06239. . . Train Acc 0.9817
Epoch 116/200. . . Train Loss 0.06232. . . Train Acc 0.9818
Epoch 116/200. . . Train Loss 0.06226. . . Train Acc 0.9818
Epoch 116/200. . . Train Loss 0.06222. . . Train Acc 0.9818
Epoch 116/200. . . Train Loss 0.06218. . . Train Acc 0.9818
Epoch 116/200. . . Train Loss 0.06211. . . Train Acc 0.9818

Caclulating loss & accuracy on  validation set...
Epoch 116/200. . . Val Loss 7.063. . . Val Acc 0.9561
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 11% of total wrong
<Right-of-way at the > is 8% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Double curve> is 7% of total wrong
<Pedestrians> is 14% of total wrong
<Children crossing> is 11% of total wrong

Epoch 117/200. . . Train Loss 0.06206. . . Train Acc 0.9818
Epoch 117/200. . . Train Loss 0.062. . . Train Acc 0.9819
Epoch 117/200. . . Train Loss 0.06193. . . Train Acc 0.9819
Epoch 117/200. . . Train Loss 0.06187. . . Train Acc 0.9819
Epoch 117/200. . . Train Loss 0.06181. . . Train Acc 0.9819

Caclulating loss & accuracy on  validation set...
Epoch 117/200. . . Val Loss 10.21. . . Val Acc 0.9417

<Speed limit (20km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 9% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 7% of total wrong
<Pedestrians> is 10% of total wrong
<Children crossing> is 7% of total wrong

Epoch 118/200. . . Train Loss 0.06176. . . Train Acc 0.9819
Epoch 118/200. . . Train Loss 0.06169. . . Train Acc 0.9819
Epoch 118/200. . . Train Loss 0.06163. . . Train Acc 0.982
Epoch 118/200. . . Train Loss 0.06158. . . Train Acc 0.982
Epoch 118/200. . . Train Loss 0.06152. . . Train Acc 0.982

Caclulating loss & accuracy on  validation set...
Epoch 118/200. . . Val Loss 7.781. . . Val Acc 0.9531
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<Yield> is 6% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Pedestrians> is 6% of total wrong
<Children crossing> is 7% of total wrong
<Turn left ahead> is 12% of total wrong

Epoch 119/200. . . Train Loss 0.06145. . . Train Acc 0.982
Epoch 119/200. . . Train Loss 0.06138. . . Train Acc 0.982
Epoch 119/200. . . Train Loss 0.06133. . . Train Acc 0.982
Epoch 119/200. . . Train Loss 0.06127. . . Train Acc 0.9821
Epoch 119/200. . . Train Loss 0.06121. . . Train Acc 0.9821
Epoch 119/200. . . Train Loss 0.06115. . . Train Acc 0.9821

Caclulating loss & accuracy on  validation set...
Epoch 119/200. . . Val Loss 9.246. . . Val Acc 0.9424

<Speed limit (120km/h> is 9% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 9% of total wrong
<Children crossing> is 12% of total wrong
<Roundabout mandatory> is 7% of total wrong

Epoch 120/200. . . Train Loss 0.06108. . . Train Acc 0.9821
Epoch 120/200. . . Train Loss 0.06104. . . Train Acc 0.9821
Epoch 120/200. . . Train Loss 0.061. . . Train Acc 0.9821
Epoch 120/200. . . Train Loss 0.06097. . . Train Acc 0.9822
Epoch 120/200. . . Train Loss 0.0609. . . Train Acc 0.9822

Caclulating loss & accuracy on  validation set...
Epoch 120/200. . . Val Loss 9.005. . . Val Acc 0.9597
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (120km/h> is 16% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Road work> is 17% of total wrong

Displaying validation samples...
Epoch 121/200. . . Train Loss 0.06084. . . Train Acc 0.9822
Epoch 121/200. . . Train Loss 0.06079. . . Train Acc 0.9822
Epoch 121/200. . . Train Loss 0.06072. . . Train Acc 0.9822
Epoch 121/200. . . Train Loss 0.06066. . . Train Acc 0.9823
Epoch 121/200. . . Train Loss 0.0606. . . Train Acc 0.9823
Epoch 121/200. . . Train Loss 0.06054. . . Train Acc 0.9823

Caclulating loss & accuracy on  validation set...
Epoch 121/200. . . Val Loss 9.552. . . Val Acc 0.9525
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 6% of total wrong
<Children crossing> is 6% of total wrong
<Roundabout mandatory> is 15% of total wrong

Epoch 122/200. . . Train Loss 0.06048. . . Train Acc 0.9823
Epoch 122/200. . . Train Loss 0.06042. . . Train Acc 0.9823
Epoch 122/200. . . Train Loss 0.06037. . . Train Acc 0.9823
Epoch 122/200. . . Train Loss 0.06032. . . Train Acc 0.9824
Epoch 122/200. . . Train Loss 0.06027. . . Train Acc 0.9824

Caclulating loss & accuracy on  validation set...
Epoch 122/200. . . Val Loss 12.19. . . Val Acc 0.9394

<Speed limit (80km/h)> is 18% of total wrong
<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 15% of total wrong
<Pedestrians> is 6% of total wrong
<Children crossing> is 6% of total wrong

Epoch 123/200. . . Train Loss 0.06023. . . Train Acc 0.9824
Epoch 123/200. . . Train Loss 0.06017. . . Train Acc 0.9824
Epoch 123/200. . . Train Loss 0.06011. . . Train Acc 0.9824
Epoch 123/200. . . Train Loss 0.06006. . . Train Acc 0.9824
Epoch 123/200. . . Train Loss 0.06. . . Train Acc 0.9825
Epoch 123/200. . . Train Loss 0.05994. . . Train Acc 0.9825

Caclulating loss & accuracy on  validation set...
Epoch 123/200. . . Val Loss 13.02. . . Val Acc 0.9412

<Speed limit (80km/h)> is 8% of total wrong
<Speed limit (120km/h> is 14% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 11% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 7% of total wrong
<Children crossing> is 8% of total wrong
<Beware of ice/snow> is 5% of total wrong

Epoch 124/200. . . Train Loss 0.05988. . . Train Acc 0.9825
Epoch 124/200. . . Train Loss 0.05982. . . Train Acc 0.9825
Epoch 124/200. . . Train Loss 0.05976. . . Train Acc 0.9825
Epoch 124/200. . . Train Loss 0.05971. . . Train Acc 0.9825
Epoch 124/200. . . Train Loss 0.05965. . . Train Acc 0.9826

Caclulating loss & accuracy on  validation set...
Epoch 124/200. . . Val Loss 8.341. . . Val Acc 0.962
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 9% of total wrong
<Beware of ice/snow> is 16% of total wrong

Epoch 125/200. . . Train Loss 0.05959. . . Train Acc 0.9826
Epoch 125/200. . . Train Loss 0.05954. . . Train Acc 0.9826
Epoch 125/200. . . Train Loss 0.05947. . . Train Acc 0.9826
Epoch 125/200. . . Train Loss 0.05943. . . Train Acc 0.9826
Epoch 125/200. . . Train Loss 0.05937. . . Train Acc 0.9826
Epoch 125/200. . . Train Loss 0.05931. . . Train Acc 0.9827

Caclulating loss & accuracy on  validation set...
Epoch 125/200. . . Val Loss 10.01. . . Val Acc 0.9526
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 7% of total wrong
<End of speed limit (> is 7% of total wrong
<Speed limit (120km/h> is 12% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 7% of total wrong
<Turn left ahead> is 7% of total wrong
<Roundabout mandatory> is 8% of total wrong

Displaying validation samples...
Epoch 126/200. . . Train Loss 0.05925. . . Train Acc 0.9827
Epoch 126/200. . . Train Loss 0.05919. . . Train Acc 0.9827
Epoch 126/200. . . Train Loss 0.05914. . . Train Acc 0.9827
Epoch 126/200. . . Train Loss 0.05908. . . Train Acc 0.9827
Epoch 126/200. . . Train Loss 0.05902. . . Train Acc 0.9827

Caclulating loss & accuracy on  validation set...
Epoch 126/200. . . Val Loss 9.476. . . Val Acc 0.9525
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (20km/h)> is 8% of total wrong
<Speed limit (80km/h)> is 10% of total wrong
<Speed limit (120km/h> is 18% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 6% of total wrong
<Pedestrians> is 7% of total wrong
<Roundabout mandatory> is 11% of total wrong

Epoch 127/200. . . Train Loss 0.05896. . . Train Acc 0.9828
Epoch 127/200. . . Train Loss 0.05891. . . Train Acc 0.9828
Epoch 127/200. . . Train Loss 0.05885. . . Train Acc 0.9828
Epoch 127/200. . . Train Loss 0.0588. . . Train Acc 0.9828
Epoch 127/200. . . Train Loss 0.05874. . . Train Acc 0.9828

Caclulating loss & accuracy on  validation set...
Epoch 127/200. . . Val Loss 9.505. . . Val Acc 0.9552
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 16% of total wrong
<Right-of-way at the > is 12% of total wrong
<Vehicles over 3.5 me> is 10% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 7% of total wrong
<Beware of ice/snow> is 8% of total wrong

Epoch 128/200. . . Train Loss 0.05869. . . Train Acc 0.9828
Epoch 128/200. . . Train Loss 0.05864. . . Train Acc 0.9828
Epoch 128/200. . . Train Loss 0.05859. . . Train Acc 0.9829
Epoch 128/200. . . Train Loss 0.05854. . . Train Acc 0.9829
Epoch 128/200. . . Train Loss 0.05849. . . Train Acc 0.9829
Epoch 128/200. . . Train Loss 0.05843. . . Train Acc 0.9829

Caclulating loss & accuracy on  validation set...
Epoch 128/200. . . Val Loss 10.05. . . Val Acc 0.968
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (120km/h> is 9% of total wrong
<No entry> is 21% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Double curve> is 9% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 5% of total wrong

Epoch 129/200. . . Train Loss 0.05837. . . Train Acc 0.9829
Epoch 129/200. . . Train Loss 0.05831. . . Train Acc 0.9829
Epoch 129/200. . . Train Loss 0.05825. . . Train Acc 0.983
Epoch 129/200. . . Train Loss 0.0582. . . Train Acc 0.983
Epoch 129/200. . . Train Loss 0.05815. . . Train Acc 0.983

Caclulating loss & accuracy on  validation set...
Epoch 129/200. . . Val Loss 8.19. . . Val Acc 0.9685
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 6% of total wrong
<Bicycles crossing> is 6% of total wrong

Epoch 130/200. . . Train Loss 0.0581. . . Train Acc 0.983
Epoch 130/200. . . Train Loss 0.05804. . . Train Acc 0.983
Epoch 130/200. . . Train Loss 0.05799. . . Train Acc 0.983
Epoch 130/200. . . Train Loss 0.05794. . . Train Acc 0.983
Epoch 130/200. . . Train Loss 0.0579. . . Train Acc 0.9831
Epoch 130/200. . . Train Loss 0.05784. . . Train Acc 0.9831

Caclulating loss & accuracy on  validation set...
Epoch 130/200. . . Val Loss 12.64. . . Val Acc 0.9567
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<Right-of-way at the > is 6% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 16% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 6% of total wrong

Displaying validation samples...
Epoch 131/200. . . Train Loss 0.05779. . . Train Acc 0.9831
Epoch 131/200. . . Train Loss 0.05774. . . Train Acc 0.9831
Epoch 131/200. . . Train Loss 0.0577. . . Train Acc 0.9831
Epoch 131/200. . . Train Loss 0.05764. . . Train Acc 0.9831
Epoch 131/200. . . Train Loss 0.05758. . . Train Acc 0.9832

Caclulating loss & accuracy on  validation set...
Epoch 131/200. . . Val Loss 10.33. . . Val Acc 0.9449

<Speed limit (80km/h)> is 16% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<No passing> is 12% of total wrong
<Vehicles over 3.5 me> is 7% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 6% of total wrong

Epoch 132/200. . . Train Loss 0.05752. . . Train Acc 0.9832
Epoch 132/200. . . Train Loss 0.05746. . . Train Acc 0.9832
Epoch 132/200. . . Train Loss 0.0574. . . Train Acc 0.9832
Epoch 132/200. . . Train Loss 0.05735. . . Train Acc 0.9832
Epoch 132/200. . . Train Loss 0.0573. . . Train Acc 0.9832
Epoch 132/200. . . Train Loss 0.05726. . . Train Acc 0.9832

Caclulating loss & accuracy on  validation set...
Epoch 132/200. . . Val Loss 6.179. . . Val Acc 0.9604
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Model Saved!

<End of speed limit (> is 9% of total wrong
<Right-of-way at the > is 9% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 7% of total wrong
<Pedestrians> is 10% of total wrong

Epoch 133/200. . . Train Loss 0.05721. . . Train Acc 0.9833
Epoch 133/200. . . Train Loss 0.05715. . . Train Acc 0.9833
Epoch 133/200. . . Train Loss 0.05711. . . Train Acc 0.9833
Epoch 133/200. . . Train Loss 0.05706. . . Train Acc 0.9833
Epoch 133/200. . . Train Loss 0.05701. . . Train Acc 0.9833

Caclulating loss & accuracy on  validation set...
Epoch 133/200. . . Val Loss 10.45. . . Val Acc 0.9441

<Speed limit (80km/h)> is 15% of total wrong
<Speed limit (120km/h> is 13% of total wrong
<No passing> is 5% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 6% of total wrong
<Pedestrians> is 5% of total wrong

Epoch 134/200. . . Train Loss 0.05697. . . Train Acc 0.9833
Epoch 134/200. . . Train Loss 0.05693. . . Train Acc 0.9833
Epoch 134/200. . . Train Loss 0.05688. . . Train Acc 0.9834
Epoch 134/200. . . Train Loss 0.05683. . . Train Acc 0.9834
Epoch 134/200. . . Train Loss 0.05678. . . Train Acc 0.9834

Caclulating loss & accuracy on  validation set...
Epoch 134/200. . . Val Loss 9.829. . . Val Acc 0.9574
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Model Saved!

<Speed limit (30km/h)> is 12% of total wrong
<Speed limit (80km/h)> is 22% of total wrong
<Right-of-way at the > is 7% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 6% of total wrong
<Beware of ice/snow> is 9% of total wrong

Epoch 135/200. . . Train Loss 0.05673. . . Train Acc 0.9834
Epoch 135/200. . . Train Loss 0.05669. . . Train Acc 0.9834
Epoch 135/200. . . Train Loss 0.05663. . . Train Acc 0.9834
Epoch 135/200. . . Train Loss 0.0566. . . Train Acc 0.9834
Epoch 135/200. . . Train Loss 0.05655. . . Train Acc 0.9835
Epoch 135/200. . . Train Loss 0.05649. . . Train Acc 0.9835

Caclulating loss & accuracy on  validation set...
Epoch 135/200. . . Val Loss 7.944. . . Val Acc 0.9614
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 14% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 14% of total wrong

Displaying validation samples...
Epoch 136/200. . . Train Loss 0.05644. . . Train Acc 0.9835
Epoch 136/200. . . Train Loss 0.05639. . . Train Acc 0.9835
Epoch 136/200. . . Train Loss 0.05633. . . Train Acc 0.9835
Epoch 136/200. . . Train Loss 0.05628. . . Train Acc 0.9835
Epoch 136/200. . . Train Loss 0.05623. . . Train Acc 0.9836

Caclulating loss & accuracy on  validation set...
Epoch 136/200. . . Val Loss 13.28. . . Val Acc 0.9526
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 7% of total wrong
<Speed limit (120km/h> is 13% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 6% of total wrong
<Children crossing> is 13% of total wrong
<Beware of ice/snow> is 9% of total wrong

Epoch 137/200. . . Train Loss 0.05619. . . Train Acc 0.9836
Epoch 137/200. . . Train Loss 0.05615. . . Train Acc 0.9836
Epoch 137/200. . . Train Loss 0.0561. . . Train Acc 0.9836
Epoch 137/200. . . Train Loss 0.05604. . . Train Acc 0.9836
Epoch 137/200. . . Train Loss 0.056. . . Train Acc 0.9836
Epoch 137/200. . . Train Loss 0.05595. . . Train Acc 0.9836

Caclulating loss & accuracy on  validation set...
Epoch 137/200. . . Val Loss 15.77. . . Val Acc 0.9605
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<No entry> is 17% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 5% of total wrong
<Children crossing> is 10% of total wrong
<Beware of ice/snow> is 12% of total wrong

Epoch 138/200. . . Train Loss 0.0559. . . Train Acc 0.9837
Epoch 138/200. . . Train Loss 0.05586. . . Train Acc 0.9837
Epoch 138/200. . . Train Loss 0.05581. . . Train Acc 0.9837
Epoch 138/200. . . Train Loss 0.05575. . . Train Acc 0.9837
Epoch 138/200. . . Train Loss 0.05571. . . Train Acc 0.9837

Caclulating loss & accuracy on  validation set...
Epoch 138/200. . . Val Loss 11.93. . . Val Acc 0.966
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 8% of total wrong
<Speed limit (80km/h)> is 8% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 18% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 6% of total wrong

Epoch 139/200. . . Train Loss 0.05566. . . Train Acc 0.9837
Epoch 139/200. . . Train Loss 0.05562. . . Train Acc 0.9837
Epoch 139/200. . . Train Loss 0.05557. . . Train Acc 0.9837
Epoch 139/200. . . Train Loss 0.05552. . . Train Acc 0.9838
Epoch 139/200. . . Train Loss 0.05549. . . Train Acc 0.9838
Epoch 139/200. . . Train Loss 0.05545. . . Train Acc 0.9838

Caclulating loss & accuracy on  validation set...
Epoch 139/200. . . Val Loss 13.7. . . Val Acc 0.9535
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 9% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 7% of total wrong
<Roundabout mandatory> is 11% of total wrong

Epoch 140/200. . . Train Loss 0.0554. . . Train Acc 0.9838
Epoch 140/200. . . Train Loss 0.05536. . . Train Acc 0.9838
Epoch 140/200. . . Train Loss 0.05531. . . Train Acc 0.9838
Epoch 140/200. . . Train Loss 0.05527. . . Train Acc 0.9838
Epoch 140/200. . . Train Loss 0.05523. . . Train Acc 0.9838

Caclulating loss & accuracy on  validation set...
Epoch 140/200. . . Val Loss 15.47. . . Val Acc 0.9456

<Speed limit (80km/h)> is 10% of total wrong
<Speed limit (120km/h> is 13% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 9% of total wrong
<Beware of ice/snow> is 5% of total wrong

Displaying validation samples...
Epoch 141/200. . . Train Loss 0.05518. . . Train Acc 0.9839
Epoch 141/200. . . Train Loss 0.05513. . . Train Acc 0.9839
Epoch 141/200. . . Train Loss 0.05508. . . Train Acc 0.9839
Epoch 141/200. . . Train Loss 0.05504. . . Train Acc 0.9839
Epoch 141/200. . . Train Loss 0.055. . . Train Acc 0.9839
Epoch 141/200. . . Train Loss 0.05496. . . Train Acc 0.9839

Caclulating loss & accuracy on  validation set...
Epoch 141/200. . . Val Loss 10.93. . . Val Acc 0.9586
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 17% of total wrong
<Pedestrians> is 8% of total wrong
<Beware of ice/snow> is 12% of total wrong

Epoch 142/200. . . Train Loss 0.05492. . . Train Acc 0.9839
Epoch 142/200. . . Train Loss 0.05487. . . Train Acc 0.984
Epoch 142/200. . . Train Loss 0.05482. . . Train Acc 0.984
Epoch 142/200. . . Train Loss 0.05478. . . Train Acc 0.984
Epoch 142/200. . . Train Loss 0.05473. . . Train Acc 0.984

Caclulating loss & accuracy on  validation set...
Epoch 142/200. . . Val Loss 8.805. . . Val Acc 0.967
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (80km/h)> is 10% of total wrong
<Vehicles over 3.5 me> is 8% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 14% of total wrong
<Roundabout mandatory> is 17% of total wrong

Epoch 143/200. . . Train Loss 0.05468. . . Train Acc 0.984
Epoch 143/200. . . Train Loss 0.05464. . . Train Acc 0.984
Epoch 143/200. . . Train Loss 0.05459. . . Train Acc 0.984
Epoch 143/200. . . Train Loss 0.05456. . . Train Acc 0.984
Epoch 143/200. . . Train Loss 0.05451. . . Train Acc 0.9841

Caclulating loss & accuracy on  validation set...
Epoch 143/200. . . Val Loss 8.543. . . Val Acc 0.9659
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (120km/h> is 5% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 24% of total wrong
<Pedestrians> is 7% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 144/200. . . Train Loss 0.05446. . . Train Acc 0.9841
Epoch 144/200. . . Train Loss 0.05442. . . Train Acc 0.9841
Epoch 144/200. . . Train Loss 0.05438. . . Train Acc 0.9841
Epoch 144/200. . . Train Loss 0.05433. . . Train Acc 0.9841
Epoch 144/200. . . Train Loss 0.05429. . . Train Acc 0.9841
Epoch 144/200. . . Train Loss 0.05425. . . Train Acc 0.9841

Caclulating loss & accuracy on  validation set...
Epoch 144/200. . . Val Loss 9.601. . . Val Acc 0.9583
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (100km/h> is 5% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Double curve> is 14% of total wrong
<Road work> is 18% of total wrong
<Pedestrians> is 5% of total wrong
<Beware of ice/snow> is 6% of total wrong

Epoch 145/200. . . Train Loss 0.0542. . . Train Acc 0.9841
Epoch 145/200. . . Train Loss 0.05415. . . Train Acc 0.9842
Epoch 145/200. . . Train Loss 0.0541. . . Train Acc 0.9842
Epoch 145/200. . . Train Loss 0.05407. . . Train Acc 0.9842
Epoch 145/200. . . Train Loss 0.05403. . . Train Acc 0.9842

Caclulating loss & accuracy on  validation set...
Epoch 145/200. . . Val Loss 13.54. . . Val Acc 0.9588
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (120km/h> is 7% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 10% of total wrong
<Roundabout mandatory> is 7% of total wrong

Displaying validation samples...
Epoch 146/200. . . Train Loss 0.054. . . Train Acc 0.9842
Epoch 146/200. . . Train Loss 0.05397. . . Train Acc 0.9842
Epoch 146/200. . . Train Loss 0.05393. . . Train Acc 0.9842
Epoch 146/200. . . Train Loss 0.0539. . . Train Acc 0.9842
Epoch 146/200. . . Train Loss 0.05386. . . Train Acc 0.9843
Epoch 146/200. . . Train Loss 0.05382. . . Train Acc 0.9843

Caclulating loss & accuracy on  validation set...
Epoch 146/200. . . Val Loss 14.01. . . Val Acc 0.9619
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Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<End of speed limit (> is 6% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 7% of total wrong
<Beware of ice/snow> is 9% of total wrong
<Turn left ahead> is 6% of total wrong

Epoch 147/200. . . Train Loss 0.05378. . . Train Acc 0.9843
Epoch 147/200. . . Train Loss 0.05373. . . Train Acc 0.9843
Epoch 147/200. . . Train Loss 0.05369. . . Train Acc 0.9843
Epoch 147/200. . . Train Loss 0.05364. . . Train Acc 0.9843
Epoch 147/200. . . Train Loss 0.05361. . . Train Acc 0.9843

Caclulating loss & accuracy on  validation set...
Epoch 147/200. . . Val Loss 14.85. . . Val Acc 0.9559
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (20km/h)> is 10% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 14% of total wrong
<Roundabout mandatory> is 9% of total wrong

Epoch 148/200. . . Train Loss 0.05356. . . Train Acc 0.9843
Epoch 148/200. . . Train Loss 0.05351. . . Train Acc 0.9844
Epoch 148/200. . . Train Loss 0.05347. . . Train Acc 0.9844
Epoch 148/200. . . Train Loss 0.05344. . . Train Acc 0.9844
Epoch 148/200. . . Train Loss 0.05339. . . Train Acc 0.9844
Epoch 148/200. . . Train Loss 0.05336. . . Train Acc 0.9844

Caclulating loss & accuracy on  validation set...
Epoch 148/200. . . Val Loss 24.02. . . Val Acc 0.9628
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Model Saved!

<Speed limit (50km/h)> is 9% of total wrong
<Stop> is 7% of total wrong
<No entry> is 18% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 12% of total wrong
<Roundabout mandatory> is 8% of total wrong

Epoch 149/200. . . Train Loss 0.05333. . . Train Acc 0.9844
Epoch 149/200. . . Train Loss 0.05328. . . Train Acc 0.9844
Epoch 149/200. . . Train Loss 0.05324. . . Train Acc 0.9844
Epoch 149/200. . . Train Loss 0.05319. . . Train Acc 0.9844
Epoch 149/200. . . Train Loss 0.05316. . . Train Acc 0.9845

Caclulating loss & accuracy on  validation set...
Epoch 149/200. . . Val Loss 15.77. . . Val Acc 0.9448

<Speed limit (80km/h)> is 11% of total wrong
<End of speed limit (> is 5% of total wrong
<No passing> is 6% of total wrong
<Vehicles over 3.5 me> is 7% of total wrong
<No entry> is 8% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Road work> is 11% of total wrong
<Beware of ice/snow> is 6% of total wrong
<Ahead only> is 5% of total wrong

Epoch 150/200. . . Train Loss 0.05312. . . Train Acc 0.9845
Epoch 150/200. . . Train Loss 0.05308. . . Train Acc 0.9845
Epoch 150/200. . . Train Loss 0.05303. . . Train Acc 0.9845
Epoch 150/200. . . Train Loss 0.05299. . . Train Acc 0.9845
Epoch 150/200. . . Train Loss 0.05295. . . Train Acc 0.9845
Epoch 150/200. . . Train Loss 0.05291. . . Train Acc 0.9845

Caclulating loss & accuracy on  validation set...
Epoch 150/200. . . Val Loss 12.74. . . Val Acc 0.9613
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Model Saved!

<Speed limit (30km/h)> is 7% of total wrong
<Speed limit (60km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 11% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 10% of total wrong
<Pedestrians> is 7% of total wrong
<Roundabout mandatory> is 5% of total wrong

Displaying validation samples...
Epoch 151/200. . . Train Loss 0.05287. . . Train Acc 0.9845
Epoch 151/200. . . Train Loss 0.05282. . . Train Acc 0.9846
Epoch 151/200. . . Train Loss 0.05278. . . Train Acc 0.9846
Epoch 151/200. . . Train Loss 0.05273. . . Train Acc 0.9846
Epoch 151/200. . . Train Loss 0.05269. . . Train Acc 0.9846

Caclulating loss & accuracy on  validation set...
Epoch 151/200. . . Val Loss 16.69. . . Val Acc 0.9606
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Model Saved!

<Speed limit (80km/h)> is 9% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Road work> is 21% of total wrong
<Pedestrians> is 8% of total wrong
<Roundabout mandatory> is 11% of total wrong

Epoch 152/200. . . Train Loss 0.05265. . . Train Acc 0.9846
Epoch 152/200. . . Train Loss 0.0526. . . Train Acc 0.9846
Epoch 152/200. . . Train Loss 0.05256. . . Train Acc 0.9846
Epoch 152/200. . . Train Loss 0.05252. . . Train Acc 0.9846
Epoch 152/200. . . Train Loss 0.05248. . . Train Acc 0.9847

Caclulating loss & accuracy on  validation set...
Epoch 152/200. . . Val Loss 21.84. . . Val Acc 0.9358

<Speed limit (120km/h> is 13% of total wrong
<No entry> is 10% of total wrong
<General caution> is 5% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 8% of total wrong
<Pedestrians> is 9% of total wrong
<Beware of ice/snow> is 11% of total wrong

Epoch 153/200. . . Train Loss 0.05243. . . Train Acc 0.9847
Epoch 153/200. . . Train Loss 0.05239. . . Train Acc 0.9847
Epoch 153/200. . . Train Loss 0.05236. . . Train Acc 0.9847
Epoch 153/200. . . Train Loss 0.05233. . . Train Acc 0.9847
Epoch 153/200. . . Train Loss 0.05229. . . Train Acc 0.9847
Epoch 153/200. . . Train Loss 0.05225. . . Train Acc 0.9847

Caclulating loss & accuracy on  validation set...
Epoch 153/200. . . Val Loss 14.45. . . Val Acc 0.9554
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Model Saved!

<Speed limit (20km/h)> is 6% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 15% of total wrong
<Road work> is 12% of total wrong
<Wild animals crossin> is 9% of total wrong
<Roundabout mandatory> is 8% of total wrong

Epoch 154/200. . . Train Loss 0.05222. . . Train Acc 0.9847
Epoch 154/200. . . Train Loss 0.05218. . . Train Acc 0.9847
Epoch 154/200. . . Train Loss 0.05214. . . Train Acc 0.9848
Epoch 154/200. . . Train Loss 0.0521. . . Train Acc 0.9848
Epoch 154/200. . . Train Loss 0.05207. . . Train Acc 0.9848

Caclulating loss & accuracy on  validation set...
Epoch 154/200. . . Val Loss 17.76. . . Val Acc 0.9511
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 17% of total wrong
<End of speed limit (> is 6% of total wrong
<No entry> is 8% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 9% of total wrong
<Roundabout mandatory> is 10% of total wrong

Epoch 155/200. . . Train Loss 0.05203. . . Train Acc 0.9848
Epoch 155/200. . . Train Loss 0.05199. . . Train Acc 0.9848
Epoch 155/200. . . Train Loss 0.05196. . . Train Acc 0.9848
Epoch 155/200. . . Train Loss 0.05192. . . Train Acc 0.9848
Epoch 155/200. . . Train Loss 0.05187. . . Train Acc 0.9848
Epoch 155/200. . . Train Loss 0.05183. . . Train Acc 0.9849

Caclulating loss & accuracy on  validation set...
Epoch 155/200. . . Val Loss 16.37. . . Val Acc 0.9561
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 10% of total wrong
<Speed limit (120km/h> is 18% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 11% of total wrong
<Roundabout mandatory> is 11% of total wrong

Displaying validation samples...
Epoch 156/200. . . Train Loss 0.05179. . . Train Acc 0.9849
Epoch 156/200. . . Train Loss 0.05176. . . Train Acc 0.9849
Epoch 156/200. . . Train Loss 0.05172. . . Train Acc 0.9849
Epoch 156/200. . . Train Loss 0.05169. . . Train Acc 0.9849
Epoch 156/200. . . Train Loss 0.05166. . . Train Acc 0.9849

Caclulating loss & accuracy on  validation set...
Epoch 156/200. . . Val Loss 22.79. . . Val Acc 0.9454

<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 20% of total wrong
<Speed limit (120km/h> is 9% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Children crossing> is 12% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 157/200. . . Train Loss 0.05162. . . Train Acc 0.9849
Epoch 157/200. . . Train Loss 0.05157. . . Train Acc 0.9849
Epoch 157/200. . . Train Loss 0.05153. . . Train Acc 0.9849
Epoch 157/200. . . Train Loss 0.0515. . . Train Acc 0.9849
Epoch 157/200. . . Train Loss 0.05147. . . Train Acc 0.985
Epoch 157/200. . . Train Loss 0.05142. . . Train Acc 0.985

Caclulating loss & accuracy on  validation set...
Epoch 157/200. . . Val Loss 17.91. . . Val Acc 0.963
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (80km/h)> is 13% of total wrong
<No entry> is 18% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Double curve> is 7% of total wrong
<Road work> is 12% of total wrong

Epoch 158/200. . . Train Loss 0.05139. . . Train Acc 0.985
Epoch 158/200. . . Train Loss 0.05135. . . Train Acc 0.985
Epoch 158/200. . . Train Loss 0.05131. . . Train Acc 0.985
Epoch 158/200. . . Train Loss 0.05126. . . Train Acc 0.985
Epoch 158/200. . . Train Loss 0.05123. . . Train Acc 0.985

Caclulating loss & accuracy on  validation set...
Epoch 158/200. . . Val Loss 23.23. . . Val Acc 0.9597
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (60km/h)> is 5% of total wrong
<Vehicles over 3.5 me> is 9% of total wrong
<No entry> is 15% of total wrong
<Dangerous curve to t> is 6% of total wrong
<Road work> is 14% of total wrong
<Pedestrians> is 6% of total wrong

Epoch 159/200. . . Train Loss 0.05119. . . Train Acc 0.985
Epoch 159/200. . . Train Loss 0.05116. . . Train Acc 0.985
Epoch 159/200. . . Train Loss 0.05113. . . Train Acc 0.9851
Epoch 159/200. . . Train Loss 0.05109. . . Train Acc 0.9851
Epoch 159/200. . . Train Loss 0.05105. . . Train Acc 0.9851

Caclulating loss & accuracy on  validation set...
Epoch 159/200. . . Val Loss 28.15. . . Val Acc 0.9594
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 8% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 5% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 8% of total wrong
<Roundabout mandatory> is 9% of total wrong

Epoch 160/200. . . Train Loss 0.05101. . . Train Acc 0.9851
Epoch 160/200. . . Train Loss 0.05097. . . Train Acc 0.9851
Epoch 160/200. . . Train Loss 0.05094. . . Train Acc 0.9851
Epoch 160/200. . . Train Loss 0.0509. . . Train Acc 0.9851
Epoch 160/200. . . Train Loss 0.05087. . . Train Acc 0.9851
Epoch 160/200. . . Train Loss 0.05084. . . Train Acc 0.9851

Caclulating loss & accuracy on  validation set...
Epoch 160/200. . . Val Loss 23.41. . . Val Acc 0.961
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (60km/h)> is 6% of total wrong
<No passing> is 7% of total wrong
<Vehicles over 3.5 me> is 17% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Road work> is 11% of total wrong
<Pedestrians> is 8% of total wrong

Displaying validation samples...
Epoch 161/200. . . Train Loss 0.05081. . . Train Acc 0.9852
Epoch 161/200. . . Train Loss 0.05077. . . Train Acc 0.9852
Epoch 161/200. . . Train Loss 0.05073. . . Train Acc 0.9852
Epoch 161/200. . . Train Loss 0.0507. . . Train Acc 0.9852
Epoch 161/200. . . Train Loss 0.05067. . . Train Acc 0.9852

Caclulating loss & accuracy on  validation set...
Epoch 161/200. . . Val Loss 19.84. . . Val Acc 0.952
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (30km/h)> is 5% of total wrong
<Vehicles over 3.5 me> is 6% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 7% of total wrong
<Bicycles crossing> is 6% of total wrong
<Beware of ice/snow> is 10% of total wrong

Epoch 162/200. . . Train Loss 0.05063. . . Train Acc 0.9852
Epoch 162/200. . . Train Loss 0.0506. . . Train Acc 0.9852
Epoch 162/200. . . Train Loss 0.05055. . . Train Acc 0.9852
Epoch 162/200. . . Train Loss 0.05051. . . Train Acc 0.9852
Epoch 162/200. . . Train Loss 0.05048. . . Train Acc 0.9853
Epoch 162/200. . . Train Loss 0.05044. . . Train Acc 0.9853

Caclulating loss & accuracy on  validation set...
Epoch 162/200. . . Val Loss 16.98. . . Val Acc 0.9583
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 11% of total wrong
<End of speed limit (> is 7% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 5% of total wrong
<Bicycles crossing> is 12% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 163/200. . . Train Loss 0.05041. . . Train Acc 0.9853
Epoch 163/200. . . Train Loss 0.05037. . . Train Acc 0.9853
Epoch 163/200. . . Train Loss 0.05033. . . Train Acc 0.9853
Epoch 163/200. . . Train Loss 0.0503. . . Train Acc 0.9853
Epoch 163/200. . . Train Loss 0.05027. . . Train Acc 0.9853

Caclulating loss & accuracy on  validation set...
Epoch 163/200. . . Val Loss 12.85. . . Val Acc 0.9607
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (20km/h)> is 9% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Vehicles over 3.5 me> is 6% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 17% of total wrong
<Pedestrians> is 8% of total wrong
<Beware of ice/snow> is 8% of total wrong

Epoch 164/200. . . Train Loss 0.05023. . . Train Acc 0.9853
Epoch 164/200. . . Train Loss 0.0502. . . Train Acc 0.9853
Epoch 164/200. . . Train Loss 0.05017. . . Train Acc 0.9853
Epoch 164/200. . . Train Loss 0.05013. . . Train Acc 0.9854
Epoch 164/200. . . Train Loss 0.0501. . . Train Acc 0.9854
Epoch 164/200. . . Train Loss 0.05006. . . Train Acc 0.9854

Caclulating loss & accuracy on  validation set...
Epoch 164/200. . . Val Loss 18.45. . . Val Acc 0.9604
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (80km/h)> is 20% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 17% of total wrong
<Pedestrians> is 7% of total wrong

Epoch 165/200. . . Train Loss 0.05002. . . Train Acc 0.9854
Epoch 165/200. . . Train Loss 0.04998. . . Train Acc 0.9854
Epoch 165/200. . . Train Loss 0.04995. . . Train Acc 0.9854
Epoch 165/200. . . Train Loss 0.04991. . . Train Acc 0.9854
Epoch 165/200. . . Train Loss 0.04988. . . Train Acc 0.9854

Caclulating loss & accuracy on  validation set...
Epoch 165/200. . . Val Loss 19.77. . . Val Acc 0.9676
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (80km/h)> is 12% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 15% of total wrong
<Dangerous curve to t> is 16% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 21% of total wrong
<Beware of ice/snow> is 5% of total wrong

Displaying validation samples...
Epoch 166/200. . . Train Loss 0.04985. . . Train Acc 0.9854
Epoch 166/200. . . Train Loss 0.04982. . . Train Acc 0.9854
Epoch 166/200. . . Train Loss 0.04978. . . Train Acc 0.9855
Epoch 166/200. . . Train Loss 0.04975. . . Train Acc 0.9855
Epoch 166/200. . . Train Loss 0.04972. . . Train Acc 0.9855
Epoch 166/200. . . Train Loss 0.04969. . . Train Acc 0.9855

Caclulating loss & accuracy on  validation set...
Epoch 166/200. . . Val Loss 14.8. . . Val Acc 0.961
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (20km/h)> is 13% of total wrong
<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (120km/h> is 12% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 11% of total wrong
<Road work> is 16% of total wrong

Epoch 167/200. . . Train Loss 0.04966. . . Train Acc 0.9855
Epoch 167/200. . . Train Loss 0.04963. . . Train Acc 0.9855
Epoch 167/200. . . Train Loss 0.04959. . . Train Acc 0.9855
Epoch 167/200. . . Train Loss 0.04956. . . Train Acc 0.9855
Epoch 167/200. . . Train Loss 0.04953. . . Train Acc 0.9855

Caclulating loss & accuracy on  validation set...
Epoch 167/200. . . Val Loss 25.83. . . Val Acc 0.948

<Speed limit (20km/h)> is 10% of total wrong
<Speed limit (80km/h)> is 8% of total wrong
<Speed limit (120km/h> is 12% of total wrong
<No passing> is 6% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 6% of total wrong
<Children crossing> is 9% of total wrong

Epoch 168/200. . . Train Loss 0.0495. . . Train Acc 0.9855
Epoch 168/200. . . Train Loss 0.04946. . . Train Acc 0.9856
Epoch 168/200. . . Train Loss 0.04943. . . Train Acc 0.9856
Epoch 168/200. . . Train Loss 0.0494. . . Train Acc 0.9856
Epoch 168/200. . . Train Loss 0.04936. . . Train Acc 0.9856

Caclulating loss & accuracy on  validation set...
Epoch 168/200. . . Val Loss 30.91. . . Val Acc 0.9447

<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (60km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 14% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Bumpy road> is 7% of total wrong
<Children crossing> is 15% of total wrong

Epoch 169/200. . . Train Loss 0.04933. . . Train Acc 0.9856
Epoch 169/200. . . Train Loss 0.0493. . . Train Acc 0.9856
Epoch 169/200. . . Train Loss 0.04927. . . Train Acc 0.9856
Epoch 169/200. . . Train Loss 0.04923. . . Train Acc 0.9856
Epoch 169/200. . . Train Loss 0.0492. . . Train Acc 0.9856
Epoch 169/200. . . Train Loss 0.04917. . . Train Acc 0.9856

Caclulating loss & accuracy on  validation set...
Epoch 169/200. . . Val Loss 47.29. . . Val Acc 0.9415

<Speed limit (80km/h)> is 10% of total wrong
<Right-of-way at the > is 5% of total wrong
<Vehicles over 3.5 me> is 8% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 11% of total wrong
<Pedestrians> is 5% of total wrong
<Children crossing> is 17% of total wrong

Epoch 170/200. . . Train Loss 0.04914. . . Train Acc 0.9856
Epoch 170/200. . . Train Loss 0.0491. . . Train Acc 0.9857
Epoch 170/200. . . Train Loss 0.04908. . . Train Acc 0.9857
Epoch 170/200. . . Train Loss 0.04905. . . Train Acc 0.9857
Epoch 170/200. . . Train Loss 0.04902. . . Train Acc 0.9857

Caclulating loss & accuracy on  validation set...
Epoch 170/200. . . Val Loss 28.71. . . Val Acc 0.9503
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Model Saved!

<Speed limit (80km/h)> is 7% of total wrong
<Yield> is 9% of total wrong
<Stop> is 7% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 8% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 7% of total wrong
<Children crossing> is 8% of total wrong
<Roundabout mandatory> is 11% of total wrong

Displaying validation samples...
Epoch 171/200. . . Train Loss 0.04898. . . Train Acc 0.9857
Epoch 171/200. . . Train Loss 0.04895. . . Train Acc 0.9857
Epoch 171/200. . . Train Loss 0.04893. . . Train Acc 0.9857
Epoch 171/200. . . Train Loss 0.0489. . . Train Acc 0.9857
Epoch 171/200. . . Train Loss 0.04886. . . Train Acc 0.9857
Epoch 171/200. . . Train Loss 0.04883. . . Train Acc 0.9857

Caclulating loss & accuracy on  validation set...
Epoch 171/200. . . Val Loss 28.02. . . Val Acc 0.9608
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<Right-of-way at the > is 7% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 12% of total wrong
<Pedestrians> is 9% of total wrong
<Bicycles crossing> is 5% of total wrong
<Beware of ice/snow> is 6% of total wrong

Epoch 172/200. . . Train Loss 0.0488. . . Train Acc 0.9857
Epoch 172/200. . . Train Loss 0.04876. . . Train Acc 0.9858
Epoch 172/200. . . Train Loss 0.04873. . . Train Acc 0.9858
Epoch 172/200. . . Train Loss 0.0487. . . Train Acc 0.9858
Epoch 172/200. . . Train Loss 0.04867. . . Train Acc 0.9858

Caclulating loss & accuracy on  validation set...
Epoch 172/200. . . Val Loss 31.0. . . Val Acc 0.96
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (120km/h> is 11% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Double curve> is 10% of total wrong
<Road work> is 7% of total wrong
<Pedestrians> is 9% of total wrong

Epoch 173/200. . . Train Loss 0.04864. . . Train Acc 0.9858
Epoch 173/200. . . Train Loss 0.04861. . . Train Acc 0.9858
Epoch 173/200. . . Train Loss 0.04858. . . Train Acc 0.9858
Epoch 173/200. . . Train Loss 0.04855. . . Train Acc 0.9858
Epoch 173/200. . . Train Loss 0.04851. . . Train Acc 0.9858
Epoch 173/200. . . Train Loss 0.04849. . . Train Acc 0.9858

Caclulating loss & accuracy on  validation set...
Epoch 173/200. . . Val Loss 25.73. . . Val Acc 0.958
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 8% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 15% of total wrong
<Double curve> is 10% of total wrong
<Road work> is 6% of total wrong
<Pedestrians> is 11% of total wrong

Epoch 174/200. . . Train Loss 0.04845. . . Train Acc 0.9859
Epoch 174/200. . . Train Loss 0.04843. . . Train Acc 0.9859
Epoch 174/200. . . Train Loss 0.04841. . . Train Acc 0.9859
Epoch 174/200. . . Train Loss 0.04837. . . Train Acc 0.9859
Epoch 174/200. . . Train Loss 0.04834. . . Train Acc 0.9859

Caclulating loss & accuracy on  validation set...
Epoch 174/200. . . Val Loss 25.32. . . Val Acc 0.9502
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (60km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 6% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<Vehicles over 3.5 me> is 6% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Double curve> is 5% of total wrong
<Road work> is 13% of total wrong

Epoch 175/200. . . Train Loss 0.0483. . . Train Acc 0.9859
Epoch 175/200. . . Train Loss 0.04827. . . Train Acc 0.9859
Epoch 175/200. . . Train Loss 0.04824. . . Train Acc 0.9859
Epoch 175/200. . . Train Loss 0.04822. . . Train Acc 0.9859
Epoch 175/200. . . Train Loss 0.04818. . . Train Acc 0.9859
Epoch 175/200. . . Train Loss 0.04815. . . Train Acc 0.9859

Caclulating loss & accuracy on  validation set...
Epoch 175/200. . . Val Loss 26.51. . . Val Acc 0.9507
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 5% of total wrong
<End of speed limit (> is 10% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 11% of total wrong
<Road work> is 8% of total wrong
<Pedestrians> is 9% of total wrong

Displaying validation samples...
Epoch 176/200. . . Train Loss 0.04812. . . Train Acc 0.9859
Epoch 176/200. . . Train Loss 0.04809. . . Train Acc 0.986
Epoch 176/200. . . Train Loss 0.04805. . . Train Acc 0.986
Epoch 176/200. . . Train Loss 0.04802. . . Train Acc 0.986
Epoch 176/200. . . Train Loss 0.04799. . . Train Acc 0.986

Caclulating loss & accuracy on  validation set...
Epoch 176/200. . . Val Loss 40.11. . . Val Acc 0.9381

<Speed limit (20km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 16% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 7% of total wrong

Epoch 177/200. . . Train Loss 0.04796. . . Train Acc 0.986
Epoch 177/200. . . Train Loss 0.04793. . . Train Acc 0.986
Epoch 177/200. . . Train Loss 0.0479. . . Train Acc 0.986
Epoch 177/200. . . Train Loss 0.04787. . . Train Acc 0.986
Epoch 177/200. . . Train Loss 0.04784. . . Train Acc 0.986

Caclulating loss & accuracy on  validation set...
Epoch 177/200. . . Val Loss 25.39. . . Val Acc 0.9624
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<Right-of-way at the > is 6% of total wrong
<No entry> is 15% of total wrong
<Dangerous curve to t> is 18% of total wrong
<Road work> is 7% of total wrong
<Pedestrians> is 5% of total wrong

Epoch 178/200. . . Train Loss 0.04781. . . Train Acc 0.986
Epoch 178/200. . . Train Loss 0.04778. . . Train Acc 0.986
Epoch 178/200. . . Train Loss 0.04775. . . Train Acc 0.9861
Epoch 178/200. . . Train Loss 0.04771. . . Train Acc 0.9861
Epoch 178/200. . . Train Loss 0.04769. . . Train Acc 0.9861
Epoch 178/200. . . Train Loss 0.04766. . . Train Acc 0.9861

Caclulating loss & accuracy on  validation set...
Epoch 178/200. . . Val Loss 36.12. . . Val Acc 0.9449

<Speed limit (30km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 7% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 5% of total wrong
<Children crossing> is 10% of total wrong
<Turn left ahead> is 6% of total wrong

Epoch 179/200. . . Train Loss 0.04763. . . Train Acc 0.9861
Epoch 179/200. . . Train Loss 0.0476. . . Train Acc 0.9861
Epoch 179/200. . . Train Loss 0.04758. . . Train Acc 0.9861
Epoch 179/200. . . Train Loss 0.04755. . . Train Acc 0.9861
Epoch 179/200. . . Train Loss 0.04752. . . Train Acc 0.9861

Caclulating loss & accuracy on  validation set...
Epoch 179/200. . . Val Loss 38.7. . . Val Acc 0.9573
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 12% of total wrong
<Speed limit (120km/h> is 5% of total wrong
<No entry> is 16% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 18% of total wrong
<Pedestrians> is 7% of total wrong
<Children crossing> is 7% of total wrong

Epoch 180/200. . . Train Loss 0.0475. . . Train Acc 0.9861
Epoch 180/200. . . Train Loss 0.04747. . . Train Acc 0.9861
Epoch 180/200. . . Train Loss 0.04744. . . Train Acc 0.9862
Epoch 180/200. . . Train Loss 0.04741. . . Train Acc 0.9862
Epoch 180/200. . . Train Loss 0.04738. . . Train Acc 0.9862
Epoch 180/200. . . Train Loss 0.04734. . . Train Acc 0.9862

Caclulating loss & accuracy on  validation set...
Epoch 180/200. . . Val Loss 39.14. . . Val Acc 0.9511
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 11% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 18% of total wrong
<Road work> is 13% of total wrong
<Children crossing> is 6% of total wrong
<Turn right ahead> is 6% of total wrong

Displaying validation samples...
Epoch 181/200. . . Train Loss 0.04731. . . Train Acc 0.9862
Epoch 181/200. . . Train Loss 0.04728. . . Train Acc 0.9862
Epoch 181/200. . . Train Loss 0.04725. . . Train Acc 0.9862
Epoch 181/200. . . Train Loss 0.04722. . . Train Acc 0.9862
Epoch 181/200. . . Train Loss 0.0472. . . Train Acc 0.9862

Caclulating loss & accuracy on  validation set...
Epoch 181/200. . . Val Loss 46.65. . . Val Acc 0.9548
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (80km/h)> is 13% of total wrong
<Speed limit (120km/h> is 10% of total wrong
<No entry> is 15% of total wrong
<Dangerous curve to t> is 15% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 9% of total wrong

Epoch 182/200. . . Train Loss 0.04717. . . Train Acc 0.9862
Epoch 182/200. . . Train Loss 0.04715. . . Train Acc 0.9862
Epoch 182/200. . . Train Loss 0.04712. . . Train Acc 0.9862
Epoch 182/200. . . Train Loss 0.04709. . . Train Acc 0.9863
Epoch 182/200. . . Train Loss 0.04706. . . Train Acc 0.9863
Epoch 182/200. . . Train Loss 0.04704. . . Train Acc 0.9863

Caclulating loss & accuracy on  validation set...
Epoch 182/200. . . Val Loss 44.23. . . Val Acc 0.9529
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 23% of total wrong
<No entry> is 14% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 14% of total wrong
<Beware of ice/snow> is 8% of total wrong

Epoch 183/200. . . Train Loss 0.047. . . Train Acc 0.9863
Epoch 183/200. . . Train Loss 0.04697. . . Train Acc 0.9863
Epoch 183/200. . . Train Loss 0.04694. . . Train Acc 0.9863
Epoch 183/200. . . Train Loss 0.0469. . . Train Acc 0.9863
Epoch 183/200. . . Train Loss 0.04688. . . Train Acc 0.9863

Caclulating loss & accuracy on  validation set...
Epoch 183/200. . . Val Loss 26.06. . . Val Acc 0.9605
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (30km/h)> is 9% of total wrong
<Speed limit (50km/h)> is 6% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<Right-of-way at the > is 7% of total wrong
<No entry> is 10% of total wrong
<Dangerous curve to t> is 9% of total wrong
<Road work> is 6% of total wrong
<Pedestrians> is 6% of total wrong
<Children crossing> is 15% of total wrong

Epoch 184/200. . . Train Loss 0.04685. . . Train Acc 0.9863
Epoch 184/200. . . Train Loss 0.04682. . . Train Acc 0.9863
Epoch 184/200. . . Train Loss 0.04679. . . Train Acc 0.9863
Epoch 184/200. . . Train Loss 0.04676. . . Train Acc 0.9863
Epoch 184/200. . . Train Loss 0.04674. . . Train Acc 0.9864

Caclulating loss & accuracy on  validation set...
Epoch 184/200. . . Val Loss 31.73. . . Val Acc 0.9535
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (60km/h)> is 12% of total wrong
<Speed limit (80km/h)> is 8% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<Right-of-way at the > is 7% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 16% of total wrong
<Pedestrians> is 5% of total wrong

Epoch 185/200. . . Train Loss 0.04672. . . Train Acc 0.9864
Epoch 185/200. . . Train Loss 0.04669. . . Train Acc 0.9864
Epoch 185/200. . . Train Loss 0.04666. . . Train Acc 0.9864
Epoch 185/200. . . Train Loss 0.04664. . . Train Acc 0.9864
Epoch 185/200. . . Train Loss 0.04661. . . Train Acc 0.9864
Epoch 185/200. . . Train Loss 0.04658. . . Train Acc 0.9864

Caclulating loss & accuracy on  validation set...
Epoch 185/200. . . Val Loss 39.53. . . Val Acc 0.9603
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (60km/h)> is 7% of total wrong
<Right-of-way at the > is 8% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 13% of total wrong
<Double curve> is 6% of total wrong
<Pedestrians> is 11% of total wrong

Displaying validation samples...
Epoch 186/200. . . Train Loss 0.04655. . . Train Acc 0.9864
Epoch 186/200. . . Train Loss 0.04653. . . Train Acc 0.9864
Epoch 186/200. . . Train Loss 0.0465. . . Train Acc 0.9864
Epoch 186/200. . . Train Loss 0.04647. . . Train Acc 0.9864
Epoch 186/200. . . Train Loss 0.04644. . . Train Acc 0.9864

Caclulating loss & accuracy on  validation set...
Epoch 186/200. . . Val Loss 34.03. . . Val Acc 0.9621
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<End of speed limit (> is 9% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 8% of total wrong
<Pedestrians> is 6% of total wrong
<Beware of ice/snow> is 5% of total wrong
<Roundabout mandatory> is 8% of total wrong

Epoch 187/200. . . Train Loss 0.04641. . . Train Acc 0.9865
Epoch 187/200. . . Train Loss 0.04639. . . Train Acc 0.9865
Epoch 187/200. . . Train Loss 0.04635. . . Train Acc 0.9865
Epoch 187/200. . . Train Loss 0.04633. . . Train Acc 0.9865
Epoch 187/200. . . Train Loss 0.0463. . . Train Acc 0.9865
Epoch 187/200. . . Train Loss 0.04627. . . Train Acc 0.9865

Caclulating loss & accuracy on  validation set...
Epoch 187/200. . . Val Loss 23.01. . . Val Acc 0.9637
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (60km/h)> is 5% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<Right-of-way at the > is 5% of total wrong
<Yield> is 5% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 16% of total wrong
<Road narrows on the > is 5% of total wrong
<Beware of ice/snow> is 5% of total wrong

Epoch 188/200. . . Train Loss 0.04625. . . Train Acc 0.9865
Epoch 188/200. . . Train Loss 0.04622. . . Train Acc 0.9865
Epoch 188/200. . . Train Loss 0.0462. . . Train Acc 0.9865
Epoch 188/200. . . Train Loss 0.04617. . . Train Acc 0.9865
Epoch 188/200. . . Train Loss 0.04615. . . Train Acc 0.9865

Caclulating loss & accuracy on  validation set...
Epoch 188/200. . . Val Loss 33.76. . . Val Acc 0.9662
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 14% of total wrong
<Children crossing> is 15% of total wrong
<Roundabout mandatory> is 6% of total wrong

Epoch 189/200. . . Train Loss 0.04612. . . Train Acc 0.9865
Epoch 189/200. . . Train Loss 0.04609. . . Train Acc 0.9865
Epoch 189/200. . . Train Loss 0.04606. . . Train Acc 0.9866
Epoch 189/200. . . Train Loss 0.04604. . . Train Acc 0.9866
Epoch 189/200. . . Train Loss 0.04601. . . Train Acc 0.9866
Epoch 189/200. . . Train Loss 0.04599. . . Train Acc 0.9866

Caclulating loss & accuracy on  validation set...
Epoch 189/200. . . Val Loss 37.83. . . Val Acc 0.9582
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (20km/h)> is 8% of total wrong
<Speed limit (50km/h)> is 5% of total wrong
<End of speed limit (> is 5% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<Vehicles over 3.5 me> is 6% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 7% of total wrong
<Road work> is 13% of total wrong
<Children crossing> is 5% of total wrong
<Roundabout mandatory> is 5% of total wrong

Epoch 190/200. . . Train Loss 0.04596. . . Train Acc 0.9866
Epoch 190/200. . . Train Loss 0.04593. . . Train Acc 0.9866
Epoch 190/200. . . Train Loss 0.04591. . . Train Acc 0.9866
Epoch 190/200. . . Train Loss 0.04588. . . Train Acc 0.9866
Epoch 190/200. . . Train Loss 0.04585. . . Train Acc 0.9866

Caclulating loss & accuracy on  validation set...
Epoch 190/200. . . Val Loss 28.67. . . Val Acc 0.9627
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<No entry> is 13% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 6% of total wrong
<Children crossing> is 16% of total wrong
<Roundabout mandatory> is 10% of total wrong

Displaying validation samples...
Epoch 191/200. . . Train Loss 0.04583. . . Train Acc 0.9866
Epoch 191/200. . . Train Loss 0.0458. . . Train Acc 0.9866
Epoch 191/200. . . Train Loss 0.04578. . . Train Acc 0.9866
Epoch 191/200. . . Train Loss 0.04575. . . Train Acc 0.9866
Epoch 191/200. . . Train Loss 0.04572. . . Train Acc 0.9867
Epoch 191/200. . . Train Loss 0.0457. . . Train Acc 0.9867

Caclulating loss & accuracy on  validation set...
Epoch 191/200. . . Val Loss 31.12. . . Val Acc 0.9658
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (120km/h> is 6% of total wrong
<No entry> is 15% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 9% of total wrong
<Pedestrians> is 9% of total wrong
<Children crossing> is 9% of total wrong

Epoch 192/200. . . Train Loss 0.04567. . . Train Acc 0.9867
Epoch 192/200. . . Train Loss 0.04565. . . Train Acc 0.9867
Epoch 192/200. . . Train Loss 0.04562. . . Train Acc 0.9867
Epoch 192/200. . . Train Loss 0.04559. . . Train Acc 0.9867
Epoch 192/200. . . Train Loss 0.04557. . . Train Acc 0.9867

Caclulating loss & accuracy on  validation set...
Epoch 192/200. . . Val Loss 39.1. . . Val Acc 0.9447

<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (60km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 11% of total wrong
<No entry> is 8% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Children crossing> is 20% of total wrong

Epoch 193/200. . . Train Loss 0.04554. . . Train Acc 0.9867
Epoch 193/200. . . Train Loss 0.0455. . . Train Acc 0.9867
Epoch 193/200. . . Train Loss 0.04548. . . Train Acc 0.9867
Epoch 193/200. . . Train Loss 0.04545. . . Train Acc 0.9867
Epoch 193/200. . . Train Loss 0.04543. . . Train Acc 0.9867

Caclulating loss & accuracy on  validation set...
Epoch 193/200. . . Val Loss 44.95. . . Val Acc 0.9581
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (120km/h> is 8% of total wrong
<No entry> is 12% of total wrong
<Dangerous curve to t> is 17% of total wrong
<Children crossing> is 9% of total wrong

Epoch 194/200. . . Train Loss 0.0454. . . Train Acc 0.9868
Epoch 194/200. . . Train Loss 0.04538. . . Train Acc 0.9868
Epoch 194/200. . . Train Loss 0.04535. . . Train Acc 0.9868
Epoch 194/200. . . Train Loss 0.04532. . . Train Acc 0.9868
Epoch 194/200. . . Train Loss 0.0453. . . Train Acc 0.9868
Epoch 194/200. . . Train Loss 0.04527. . . Train Acc 0.9868

Caclulating loss & accuracy on  validation set...
Epoch 194/200. . . Val Loss 44.83. . . Val Acc 0.9461

<Speed limit (50km/h)> is 5% of total wrong
<Speed limit (80km/h)> is 12% of total wrong
<End of speed limit (> is 15% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Children crossing> is 13% of total wrong

Epoch 195/200. . . Train Loss 0.04525. . . Train Acc 0.9868
Epoch 195/200. . . Train Loss 0.04522. . . Train Acc 0.9868
Epoch 195/200. . . Train Loss 0.04519. . . Train Acc 0.9868
Epoch 195/200. . . Train Loss 0.04516. . . Train Acc 0.9868
Epoch 195/200. . . Train Loss 0.04513. . . Train Acc 0.9868

Caclulating loss & accuracy on  validation set...
Epoch 195/200. . . Val Loss 50.09. . . Val Acc 0.9673
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (120km/h> is 7% of total wrong
<No entry> is 19% of total wrong
<Dangerous curve to t> is 12% of total wrong
<Double curve> is 6% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 6% of total wrong
<Children crossing> is 7% of total wrong

Displaying validation samples...
Epoch 196/200. . . Train Loss 0.04512. . . Train Acc 0.9868
Epoch 196/200. . . Train Loss 0.04508. . . Train Acc 0.9868
Epoch 196/200. . . Train Loss 0.04506. . . Train Acc 0.9868
Epoch 196/200. . . Train Loss 0.04503. . . Train Acc 0.9869
Epoch 196/200. . . Train Loss 0.04501. . . Train Acc 0.9869
Epoch 196/200. . . Train Loss 0.04499. . . Train Acc 0.9869

Caclulating loss & accuracy on  validation set...
Epoch 196/200. . . Val Loss 44.79. . . Val Acc 0.9442

<Speed limit (60km/h)> is 21% of total wrong
<Speed limit (80km/h)> is 9% of total wrong
<Speed limit (120km/h> is 8% of total wrong
<No entry> is 11% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 11% of total wrong

Epoch 197/200. . . Train Loss 0.04496. . . Train Acc 0.9869
Epoch 197/200. . . Train Loss 0.04493. . . Train Acc 0.9869
Epoch 197/200. . . Train Loss 0.04491. . . Train Acc 0.9869
Epoch 197/200. . . Train Loss 0.04488. . . Train Acc 0.9869
Epoch 197/200. . . Train Loss 0.04485. . . Train Acc 0.9869

Caclulating loss & accuracy on  validation set...
Epoch 197/200. . . Val Loss 59.61. . . Val Acc 0.9596
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (50km/h)> is 7% of total wrong
<Speed limit (80km/h)> is 5% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 17% of total wrong
<Dangerous curve to t> is 13% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 12% of total wrong

Epoch 198/200. . . Train Loss 0.04483. . . Train Acc 0.9869
Epoch 198/200. . . Train Loss 0.0448. . . Train Acc 0.9869
Epoch 198/200. . . Train Loss 0.04478. . . Train Acc 0.9869
Epoch 198/200. . . Train Loss 0.04475. . . Train Acc 0.9869
Epoch 198/200. . . Train Loss 0.04473. . . Train Acc 0.9869
Epoch 198/200. . . Train Loss 0.0447. . . Train Acc 0.987

Caclulating loss & accuracy on  validation set...
Epoch 198/200. . . Val Loss 49.5. . . Val Acc 0.9608
mkdir: cannot create directory ‘checks_96’: File exists
Model Saved!

<Speed limit (50km/h)> is 8% of total wrong
<Speed limit (120km/h> is 6% of total wrong
<No entry> is 13% of total wrong
<Dangerous curve to t> is 19% of total wrong
<Road work> is 13% of total wrong
<Pedestrians> is 11% of total wrong

Epoch 199/200. . . Train Loss 0.04468. . . Train Acc 0.987
Epoch 199/200. . . Train Loss 0.04466. . . Train Acc 0.987
Epoch 199/200. . . Train Loss 0.04464. . . Train Acc 0.987
Epoch 199/200. . . Train Loss 0.04462. . . Train Acc 0.987
Epoch 199/200. . . Train Loss 0.0446. . . Train Acc 0.987

Caclulating loss & accuracy on  validation set...
Epoch 199/200. . . Val Loss 53.25. . . Val Acc 0.9564
mkdir: cannot create directory ‘checks_95’: File exists
Model Saved!

<Speed limit (80km/h)> is 8% of total wrong
<Speed limit (120km/h> is 7% of total wrong
<No entry> is 15% of total wrong
<General caution> is 7% of total wrong
<Dangerous curve to t> is 17% of total wrong
<Road work> is 5% of total wrong
<Pedestrians> is 7% of total wrong
<Beware of ice/snow> is 6% of total wrong

Epoch 200/200. . . Train Loss 0.04457. . . Train Acc 0.987
Epoch 200/200. . . Train Loss 0.04455. . . Train Acc 0.987
Epoch 200/200. . . Train Loss 0.04453. . . Train Acc 0.987
Epoch 200/200. . . Train Loss 0.0445. . . Train Acc 0.987
Epoch 200/200. . . Train Loss 0.04447. . . Train Acc 0.987
Epoch 200/200. . . Train Loss 0.04445. . . Train Acc 0.987

Caclulating loss & accuracy on  validation set...
Epoch 200/200. . . Val Loss 58.01. . . Val Acc 0.9414

<Speed limit (80km/h)> is 12% of total wrong
<No entry> is 9% of total wrong
<Dangerous curve to t> is 10% of total wrong
<Road work> is 12% of total wrong
<Children crossing> is 11% of total wrong

Displaying validation samples...
Model finished training :)

Load best checkpoint and check accuracy on val set

In [11]:
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checks_97_new'))
    test_accs, wrong_indices = [], []
    for test_idx, test_x, test_y, test_names, _, _  in get_batches_test(X_valid, y_valid, sign_names, 
                                                                    valid_sizes, valid_coord, batch_size):
        feed = {model.x: test_x, 
                model.targets: test_y, 
                model.drop_rate: 0., 
                model.is_train: False}
        test_acc, test_cor = sess.run([model.accuracy, model.correct_pred], feed_dict=feed)
        test_accs.append(test_acc)
        wrong_idx = [test_idx[i] for i in range(len(test_cor)) if test_cor[i] == False]
        wrong_indices.append(wrong_idx)
    avg_test_acc = np.mean(test_accs)
    print('Validation Accuracy {:.4}'.format(avg_test_acc))
    print('Displaying validation samples...')
    num_images = len(X_valid)
    idx = np.arange(num_images)
    np.random.shuffle(idx)
    num_show = 5
    [view_val_with_preds(sess, X_valid[idx[i]], y_valid[idx[i]], sign_names[y_valid[idx[i]], 1], 
                         valid_sizes[idx[i]], valid_coord[idx[i]]) for i in range(num_show)]
    
wrong_indices = np.array([p for sublist in wrong_indices for p in sublist])
n_wrong = len(wrong_indices)
print('Total misclassified signs in validation set: {}'.format(n_wrong))
INFO:tensorflow:Restoring parameters from checks_97_new/tsigns.ckpt
Validation Accuracy 0.971
Displaying validation samples...
Total misclassified signs in validation set: 128

Have a gander at some of the misclassified images

In [20]:
plt.figure(figsize=(45, 45))
for i in range(80):
    plt.subplot(8,10,(i+1))
    plt.title('{:.20}'.format(sign_names[y_valid[wrong_indices[i]], 1]), fontsize=22)
    plt.imshow(X_valid[wrong_indices[i]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')

Check accuracy on test set

I'll also track the true positives, true negatives, false positives, and false negatives, then calculate the precision, recall, and specificity for each class. This can help determine how to augment data by showing the network's weak labels.

In [28]:
tp = np.zeros(n_classes)
tn = np.zeros(n_classes)
fp = np.zeros(n_classes)
fn = np.zeros(n_classes)

with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checks_97_new'))
    test_accs, wrong_indices = [], []
    for test_idx, test_x, test_y, test_names, _, _  in get_batches_test(X_test, y_test, sign_names, 
                                                                    test_sizes, test_coord, batch_size):
        feed = {model.x: test_x, 
                model.targets: test_y, 
                model.drop_rate: 0., 
                model.is_train: False}
        test_acc, test_preds, test_cor = sess.run([model.accuracy, model.preds, model.correct_pred], feed_dict=feed)
        test_preds = tf.argmax(test_preds, 1).eval()
        for i in range(len(test_cor)):
            if test_cor[i] == True:
                tp[test_y[i]] += 1
                for j in range(n_classes):
                    if j != test_y[i]:
                        tn[j] += 1
            if test_cor[i] == False:
                fp[test_preds[i]] += 1
                fn[y_test[i]] += 1
        
        test_accs.append(test_acc)
        wrong_idx = [test_idx[i] for i in range(len(test_cor)) if test_cor[i] == False]
        wrong_indices.append(wrong_idx)
    avg_test_acc = np.mean(test_accs)
    print('Test Accuracy {:.4}'.format(avg_test_acc))
    print('Displaying test samples...')
    num_images = len(X_test)
    idx = np.arange(num_images)
    np.random.shuffle(idx)
    num_show = 5
    [view_val_with_preds(sess, X_test[idx[i]], y_test[idx[i]], sign_names[y_test[idx[i]], 1], 
                         test_sizes[idx[i]], test_coord[idx[i]]) for i in range(num_show)]

wrong_indices = np.array([p for sublist in wrong_indices for p in sublist])
n_wrong = len(wrong_indices)
print('Total misclassified signs in test set: {}'.format(n_wrong))
print('')
precision = tp / (tp + fp)
recall = tp / (tp + fn)
specificity = tn / (tn + fp)
accuracy = np.sum(tp) / len(X_test)
print('Double-check accuracy: {:.4}'.format(accuracy))
print('')
for i in range(n_classes):
    print('{}:'.format(sign_names[i, 1]))
    print('     precision: {:.4}'.format(precision[i]))
    print('     recall: {:.4}'.format(recall[i]))
    print('     specificity: {:.4}'.format(specificity[i]))
    print('')
INFO:tensorflow:Restoring parameters from checks_97_new/tsigns.ckpt
Test Accuracy 0.9291
Displaying test samples...
Total misclassified signs in test set: 894

Double-check accuracy: 0.9292

Speed limit (20km/h):
     precision: 0.8551
     recall: 1.0
     specificity: 0.9991

Speed limit (30km/h):
     precision: 0.9689
     recall: 0.9263
     specificity: 0.9979

Speed limit (50km/h):
     precision: 0.9606
     recall: 1.0
     specificity: 0.9973

Speed limit (60km/h):
     precision: 0.9374
     recall: 0.9439
     specificity: 0.9976

Speed limit (70km/h):
     precision: 0.9831
     recall: 0.9088
     specificity: 0.999

Speed limit (80km/h):
     precision: 0.8804
     recall: 0.9546
     specificity: 0.9929

End of speed limit (:
     precision: 0.9813
     recall: 1.0
     specificity: 0.9998

Speed limit (100km/h:
     precision: 0.9782
     recall: 0.8838
     specificity: 0.9992

Speed limit (120km/h:
     precision: 0.9626
     recall: 1.0
     specificity: 0.9985

No passing:
     precision: 0.9958
     recall: 0.8375
     specificity: 0.9998

No passing for vehic:
     precision: 1.0
     recall: 0.9564
     specificity: 1.0

Right-of-way at the :
     precision: 0.8527
     recall: 0.8304
     specificity: 0.9942

Priority road:
     precision: 0.8242
     recall: 0.9416
     specificity: 0.9874

Yield:
     precision: 0.9664
     recall: 0.9597
     specificity: 0.9978

Stop:
     precision: 0.9756
     recall: 1.0
     specificity: 0.9995

No vehicles:
     precision: 1.0
     recall: 1.0
     specificity: 1.0

Vehicles over 3.5 me:
     precision: 0.8824
     recall: 0.838
     specificity: 0.9983

No entry:
     precision: 0.9965
     recall: 0.914
     specificity: 0.9999

General caution:
     precision: 0.9188
     recall: 0.9292
     specificity: 0.9975

Dangerous curve to t:
     precision: 0.8939
     recall: 1.0
     specificity: 0.9994

Dangerous curve to t:
     precision: 0.6294
     recall: 0.7759
     specificity: 0.9955

Double curve:
     precision: 0.9074
     recall: 0.6364
     specificity: 0.9996

Bumpy road:
     precision: 0.8571
     recall: 1.0
     specificity: 0.9984

Slippery road:
     precision: 0.8552
     recall: 0.775
     specificity: 0.9982

Road narrows on the :
     precision: 0.9425
     recall: 1.0
     specificity: 0.9996

Road work:
     precision: 0.8706
     recall: 0.9308
     specificity: 0.9938

Traffic signals:
     precision: 0.9767
     recall: 1.0
     specificity: 0.9997

Pedestrians:
     precision: 0.9667
     recall: 0.537
     specificity: 0.9999

Children crossing:
     precision: 0.8571
     recall: 1.0
     specificity: 0.9983

Bicycles crossing:
     precision: 0.7658
     recall: 1.0
     specificity: 0.9978

Beware of ice/snow:
     precision: 0.9579
     recall: 1.0
     specificity: 0.9997

Wild animals crossin:
     precision: 0.9418
     recall: 1.0
     specificity: 0.9986

End of all speed and:
     precision: 1.0
     recall: 1.0
     specificity: 1.0

Turn right ahead:
     precision: 0.9894
     recall: 0.775
     specificity: 0.9998

Turn left ahead:
     precision: 0.9836
     recall: 1.0
     specificity: 0.9998

Ahead only:
     precision: 0.9161
     recall: 0.9294
     specificity: 0.9969

Go straight or right:
     precision: 1.0
     recall: 1.0
     specificity: 1.0

Go straight or left:
     precision: 0.7662
     recall: 1.0
     specificity: 0.9985

Keep right:
     precision: 0.9735
     recall: 0.8859
     specificity: 0.9984

Keep left:
     precision: 0.8208
     recall: 1.0
     specificity: 0.9984

Roundabout mandatory:
     precision: 0.7748
     recall: 1.0
     specificity: 0.9979

End of no passing:
     precision: 1.0
     recall: 1.0
     specificity: 1.0

End of no passing by:
     precision: 0.7568
     recall: 1.0
     specificity: 0.9977

Some more misclassified images, this time on test set

In [25]:
plt.figure(figsize=(45, 45))
for i in range(100):
    plt.subplot(10,10,(i+1))
    plt.title('{:.20}'.format(sign_names[y_test[wrong_indices[i+100]], 1]), fontsize=22)
    plt.imshow(X_test[wrong_indices[i+100]])
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')

Let's see how the network performs with new images found on the web

In [11]:
eg1 = normalize(cv2.resize(mpimg.imread('traffic-signs-web/example_1.jpg'), dsize=(32,32)))
eg2 = normalize(cv2.resize(mpimg.imread('traffic-signs-web/example_2.jpg'), dsize=(32,32)))
eg3 = normalize(cv2.resize(mpimg.imread('traffic-signs-web/example_4.jpg'), dsize=(32,32)))
eg4 = normalize(cv2.resize(mpimg.imread('traffic-signs-web/example_5.jpg'), dsize=(32,32)))
eg5 = normalize(cv2.resize(mpimg.imread('traffic-signs-web/example_6.jpg'), dsize=(32,32)))
egs = [eg1, eg2, eg3, eg4, eg5]
plt.figure(figsize=(15,5))
for i in range(5):
    plt.subplot(1,5,(i+1))
    plt.imshow(egs[i])
In [56]:
def view_web_with_preds(sess, image, top_n=how_many):
    feed = {model.x: image.reshape((1,32,32,-1)), 
            model.drop_rate: 0., 
            model.is_train: False}
    pred = sess.run(model.preds, feed_dict=feed)
    top_n_pred_names = top_n_predictions(pred)
    top_n_pred_probs = top_n_predictions(pred, probs_off=False)
    
    plt.figure(figsize=(10, 8))
    plt.subplot(2,2,1)
    y_pos = np.arange(top_n)
    probabilities = np.flip(top_n_pred_probs, 0)
    plt.barh(y_pos, probabilities, align='center', color='blue', tick_label=np.flip(top_n_pred_names, 0))
    plt.xlabel('Probability')
    
    plt.subplot(2,2,2)
    plt.imshow(image)
    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off')
        
    plt.subplots_adjust(wspace=0.75)
    plt.show()
    
    return np.flip(probabilities, 0), top_n_pred_names
In [13]:
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checks_97_new'))
    for i in range(5):
        probs, top_n_names = view_web_with_preds(sess, egs[i])
        print('Top 5 Softmax Probabilities:')
        [print('     {}: {:.4}'.format(top_n_names[i], probs[i])) for i in range(5)]
INFO:tensorflow:Restoring parameters from checks_97_new/tsigns.ckpt
Top 5 Softmax Probabilities:
     Roundabout mandatory: 1.0
     Speed limit (20km/h): 0.0
     Speed limit (30km/h): 0.0
     Speed limit (50km/h): 0.0
     Speed limit (60km/h): 0.0
Top 5 Softmax Probabilities:
     Yield: 1.0
     Priority road: 1.006e-35
     Speed limit (20km/h): 0.0
     Speed limit (30km/h): 0.0
     Speed limit (50km/h): 0.0
Top 5 Softmax Probabilities:
     Ahead only: 1.0
     Speed limit (20km/h): 0.0
     Speed limit (30km/h): 0.0
     Speed limit (50km/h): 0.0
     Speed limit (60km/h): 0.0
Top 5 Softmax Probabilities:
     Turn right ahead: 1.0
     Speed limit (20km/h): 0.0
     Speed limit (30km/h): 0.0
     Speed limit (50km/h): 0.0
     Speed limit (60km/h): 0.0
Top 5 Softmax Probabilities:
     Speed limit (60km/h): 1.0
     Speed limit (20km/h): 0.0
     Speed limit (30km/h): 0.0
     Speed limit (50km/h): 0.0
     Speed limit (70km/h): 0.0

Crushing it! Now let's check a sign from the web that had low precision and recall on the test set, 'Dangerous Curve to Right'

In [59]:
print('{}:'.format(sign_names[20, 1]))
print('     precision: {:.4}'.format(precision[20]))
print('     recall: {:.4}'.format(recall[20]))
print('     specificity: {:.4}'.format(specificity[20]))

dang_curve_to_right = normalize(cv2.resize(mpimg.imread('traffic-signs-web/dangerous_curve_to_right.jpg'), dsize=(32,32)))
dang_curve_to_right2 = normalize(cv2.resize(mpimg.imread('traffic-signs-web/dangerous_curve_to_right2.jpg'), dsize=(32,32)))

with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checks_97_new'))
    probs, top_n_names = view_web_with_preds(sess, dang_curve_to_right)
    print('Top 5 Softmax Probabilities:')
    [print('     {}: {:.4}'.format(top_n_names[i], probs[i])) for i in range(5)]
    
    probs, top_n_names = view_web_with_preds(sess, dang_curve_to_right2)
    print('Top 5 Softmax Probabilities:')
    [print('     {}: {:.4}'.format(top_n_names[i], probs[i])) for i in range(5)]
Dangerous curve to the right:
     precision: 0.6294
     recall: 0.7759
     specificity: 0.9955
INFO:tensorflow:Restoring parameters from checks_97_new/tsigns.ckpt
Top 5 Softmax Probabilities:
     Dangerous curve to the right: 1.0
     Speed limit (20km/h): 0.0
     Speed limit (30km/h): 0.0
     Speed limit (50km/h): 0.0
     Speed limit (60km/h): 0.0
Top 5 Softmax Probabilities:
     Ahead only: 1.0
     Go straight or left: 4.973e-26
     Speed limit (20km/h): 0.0
     Speed limit (30km/h): 0.0
     Speed limit (50km/h): 0.0

Well, it got the first one right. At least the miss was predictable.

Finally, have a look at the network's activation output to see how it's learning

1) On Roundabout Mandatory from web

In [14]:
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checks_97_new'))
    feed = {model.x: egs[0].reshape((1,32,32,-1)), 
            model.drop_rate: 0., 
            model.is_train: False}
    relu1, relu2 = sess.run([model.relu1, model.relu2], feed_dict=feed)
    
    plt.figure(figsize=(5,5))
    plt.imshow(egs[0])
    plt.title('Original Image')
    
    plt.figure(figsize=(35,35))
    plt.suptitle('ReLU 1', fontsize=45, y=0.91)
    for i in range(64):
        plt.subplot(8,8,(i+1))
        plt.imshow(relu1[0, :, :, i], interpolation='nearest', cmap='gray')
        plt.title('FeatureMap #{}'.format(i))
        
    plt.figure(figsize=(35,35))
    plt.suptitle('ReLU 2', fontsize=45, y=0.91)
    for i in range(64):
        plt.subplot(8,8,(i+1))
        plt.imshow(relu2[0, :, :, i], interpolation='nearest', cmap='gray')
        plt.title('FeatureMap #{}'.format(i))
INFO:tensorflow:Restoring parameters from checks_97_new/tsigns.ckpt

2) On 60km/h from web

In [15]:
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checks_97_new'))
    feed = {model.x: egs[-1].reshape((1,32,32,-1)), 
            model.drop_rate: 0., 
            model.is_train: False}
    relu1, relu2 = sess.run([model.relu1, model.relu2], feed_dict=feed)
    
    plt.figure(figsize=(5,5))
    plt.imshow(egs[-1])
    plt.title('Original Image')
    
    plt.figure(figsize=(35,35))
    plt.suptitle('ReLU 1', fontsize=45, y=0.91)
    for i in range(64):
        plt.subplot(8,8,(i+1))
        plt.imshow(relu1[0, :, :, i], interpolation='nearest', cmap='gray')
        plt.title('FeatureMap #{}'.format(i))
        
    plt.figure(figsize=(35,35))
    plt.suptitle('ReLU 2', fontsize=45, y=0.91)
    for i in range(64):
        plt.subplot(8,8,(i+1))
        plt.imshow(relu2[0, :, :, i], interpolation='nearest', cmap='gray')
        plt.title('FeatureMap #{}'.format(i))
INFO:tensorflow:Restoring parameters from checks_97_new/tsigns.ckpt

3) On Dangerous Curve to Right from web

Keeping in mind how this sign had low precision and recall, notice how the network has trouble recognizing the relevant line that distinguishes it from other triangular street signs.

In [61]:
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checks_97_new'))
    feed = {model.x: dang_curve_to_right2.reshape((1,32,32,-1)), 
            model.drop_rate: 0., 
            model.is_train: False}
    relu1, relu2 = sess.run([model.relu1, model.relu2], feed_dict=feed)
    
    plt.figure(figsize=(5,5))
    plt.imshow(dang_curve_to_right2)
    plt.title('Original Image')
    
    plt.figure(figsize=(35,35))
    plt.suptitle('ReLU 1', fontsize=45, y=0.91)
    for i in range(64):
        plt.subplot(8,8,(i+1))
        plt.imshow(relu1[0, :, :, i], interpolation='nearest', cmap='gray')
        plt.title('FeatureMap #{}'.format(i))
        
    plt.figure(figsize=(35,35))
    plt.suptitle('ReLU 2', fontsize=45, y=0.91)
    for i in range(64):
        plt.subplot(8,8,(i+1))
        plt.imshow(relu2[0, :, :, i], interpolation='nearest', cmap='gray')
        plt.title('FeatureMap #{}'.format(i))
INFO:tensorflow:Restoring parameters from checks_97_new/tsigns.ckpt

4) On Van Gogh's Siesta

Because why not?

In [17]:
van_gogh = normalize(cv2.resize(mpimg.imread('traffic-signs-web/van_gogh.jpg'), dsize=(32,32)))
van_gogh_orig = mpimg.imread('traffic-signs-web/van_gogh.jpg')
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checks_97_new'))
    feed = {model.x: van_gogh.reshape((1,32,32,-1)), 
            model.drop_rate: 0., 
            model.is_train: False}
    relu1, relu2 = sess.run([model.relu1, model.relu2], feed_dict=feed)
    
    plt.figure(figsize=(5,5))
    plt.imshow(van_gogh_orig)
    plt.title('Original Image')
    
    plt.figure(figsize=(35,35))
    plt.suptitle('ReLU 1', fontsize=45, y=0.91)
    for i in range(64):
        plt.subplot(8,8,(i+1))
        plt.imshow(relu1[0, :, :, i], interpolation='nearest', cmap='gray')
        plt.title('FeatureMap #{}'.format(i))
        
    plt.figure(figsize=(35,35))
    plt.suptitle('ReLU 2', fontsize=45, y=0.91)
    for i in range(64):
        plt.subplot(8,8,(i+1))
        plt.imshow(relu2[0, :, :, i], interpolation='nearest', cmap='gray')
        plt.title('FeatureMap #{}'.format(i))
INFO:tensorflow:Restoring parameters from checks_97_new/tsigns.ckpt